Science topic

Radar - Science topic

Radar is a system using beamed and reflected radio signals to and from an object in such a way that range, bearing, and other characteristics of the object may be determined.
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We are planning to perform MIMO- SAR using FMCW Radar sensor on a moving platform. Any suggestions and inputs from anyone. Thank you.
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if it is a moving platform ( small plane?) you may be strong restrictions on the type of antenna you are using by aerodynamics (if my guess is right)
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Hello all,
I am planning to develop a radar to detect water leakages in pipes. These pipes can be present in buildings (inside walls) or underground (including concrete). Before I begin with building the radar, I intend to do extensive literature survey regarding the different problems this poses as well as current technologies in place.
It would be really helpful if any resources pertaining to above problem statement is shared.
Thanks and Regards
Abhishek
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Iam sure the following publication, which gives an overview of all leak detection methods, including ground penetrating radar and satelite radar can help you:
It analyzes all important influencing factors when it comes to detect a leak (from pipe diameter and material to water pressure and charachteristics, ground conditions, intermittent supply ....)
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How do I compute the height of tallest skyscraper of Wuhan (Wuhan Greenland center) from S1A images using radar geometry?
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Looking for alternative MIMO based automotive radars other than TI make with low cost and yet provide good performance.
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I agree with Ara Abdulsatar Assim
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We wish to write verilog code, which can be placed on an FPGA to generate FMCW radar waveform. Could, anyone suggest any code/ program pertaining to this. Thank you.
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It's better to implement the same in H SPICE FOR the above problem,
In VERILOG there may be some problems in synthesis and interfacing,
To reduce the synthesis problems I.e. interfacing we can use other platforms.
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please indicate RCS and auto locking capabilities
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the best method is Kalman filter
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I am looking for low cost radar sensors, which can detect targets up to 150 m in range, while simultaneously providing good range, velocity and angular resolutions.
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Perhaps you can try to see BMW parts or MB parts, they are the faucet in the automotive industry, the manufacturer of the part is the direction you can try.
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Looking for drone tracking radar, starting from several kilometers and 0.01 RCS
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Dear Researcher,
I have the following MATLAB codes that generate FMCW signal. However, I have two basic problem with code I appreciate it if you can help/guide me to resolve them:
1. Based on my understanding, this code generates FMCW for one Target as the dimension of the sig is 1 x N which must must be L x N (L is the number of target)
2. the Dechirped signal, which is Analog, at the receiver have to be converted to digital in my algorithm
Note/ I want to apply this time of signal (FMCW) to Direction of Arrival Estimation (DOA) algorithm
Again I highly appreciate your time and consideration to help me to overcome these uncertainness.
%%CODES for generating FMCW signal%%%%%%%%
% Compute hardware parameters from specified long-range requirements
fc = 77e9; % Center frequency (Hz)
c = physconst('LightSpeed'); % Speed of light in air (m/s)
lambda = freq2wavelen(fc,c); % Wavelength (m)
% Set the chirp duration to be 5 times the max range requirement
rangeMax = 100; % Maximum range (m)
% In general, for an FMCW radar system, the "sweep time" should be at least five to six times the round trip time
tm = 5*range2time(rangeMax,c); % Chirp duration (s)=Symbol duration (Tsym)
% Determine the waveform bandwidth from the required range resolution
rangeRes = 1; % Desired range resolution (m)
bw = rangeres2bw(rangeRes,c); % Corresponding bandwidth (Hz)
% Set the sampling rate to satisfy both the range and velocity requirements for the radar
sweepSlope = bw/tm; % FMCW sweep slope (Hz/s)
fbeatMax = range2beat(rangeMax,sweepSlope,c); % Maximum beat frequency (Hz)
vMax = 230*1000/3600; % Maximum Velocity of cars (m/s)
fdopMax = speed2dop(2*vMax,lambda); % Maximum Doppler shift (Hz)
fifMax = fbeatMax+fdopMax; % Maximum received IF (Hz)
fs = max(2*fifMax,bw); % Sampling rate (Hz)
% Configure the FMCW waveform using the waveform parameters derived from the long-range requirements
waveform = phased.FMCWWaveform('SweepTime',tm,'SweepBandwidth',bw,...
'SampleRate',fs,'NumSweeps',2,'SweepDirection','Up');
% if strcmp(waveform.SweepDirection,'Down')
% sweepSlope = -sweepSlope;
% end
N=tm*fs; % Number of fast-time samples
Nsweep = 192; % Number of slow-time samples
sigTx = waveform();
for i=1:K
doas_rad=AOA_Degree*pi/180;
A=exp(-1i*2*pi*d*(0:M-1)'*sin([doas_rad(:).']));
sigRx=A*sigTgt';
sigRx=sigRx+awgn(sigRx,SNR);
%DeChirped and conevrt it to Digital
% DesigRx=dechirp(sigRx,sigREF);
DechirpedSignal= sigTgt .* conj(sigRx);
end
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My suggestion would be to first understand your code before asking questions. In my opinion, you did not ask questions, you gave two observations. Related to your observation 2. what do you mean by analog? In computer/Matlab everything is digital. You can model analog-to-digital conversion in Matlab but signals will still be digital. Try to understand code and then pose questions.
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I am doing some research in using mm wave for sensing and i am looking for a book that can help me
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Hello Saeed,
To be honest, MMW radar is much the same as any other radar BUT enjoys the benefits and drawbacks of an unusually high operating frequency, so any good text book on radar is appropriate. The choice of a particularly high operating frequency, such as in the MMW band, does bring about certain possibilities that would be easier to implement in the MMW band than they would be at much lower frequencies, and this, in turn, means that MMW radar lends itself well to certain applications. I would suggest your choice of book be driven by which application(s) interest you. One of the big issues with MMW radar is the much higher atmospheric attenuation and bad weather attenuation and backscatter (clutter) and so many texts on MMW radar cover these issues well. A book I can highly recommend is "Principles and Applications of Millimeter-Wave Radar" by Currie and Brown published by Artech House. My copy is pretty old now (1987) but well-thumbed but would not cover new advances in MMW components.
Best regards, Clive
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How do we calculate the radiated power of an aesa radar if it is using 80 T/R modules, while each TR module produces 26 dBm of power. Will that be simple arithmetic addition to 32KW or there is some other procedure to calculate the EIRP.
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Here is the attachment.
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We have collected some data from a automotive radar sensor and a camera sensor simultaneously, and we wish to fuse the track data from both these sensors. Could any one provide the methods, approaches and techniques pertaining to this important problem and any sample codes that are available. That would help us significantly.
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Generally radar detection algorithms to work in real time would require less computational complexity. CA-CFAR is good candidate algorithm in the presence of noise and when electronic counter measures/intentional interference is present, multiple detection comes in the R-D map. We have collected some real time data from mmWave sensor in the presence of ECM. Can any one of you suggest, which one is a good detection algorithms to detect the target in this scenario?. Thank you very much.
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Track the jammer, and use information from that track to reduce the effects of the jammer on other tracks.
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I am using a radar sensor for physiological monitoring. Suppose my required physiological frequency range is A to B Hz. I find the power of the signal in A to B Hz range and also of the remaining range. Suppose the power of the required range is U and the power of the remaining range is V. Now my query is can U/V serve as an estimation of SNR?
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U is the power of the required range and V is the power of the remaining range. V are noise and physiological responses that you do not need. So V is not noise and U/V is not signal to noise ratio or SNR.
Best regards
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I'm working with a radar image and using the SNIC algorithm to segment it in Google Earth Engine. From what I have read, by viewing the region of interest and changing specific parameter values, the best result can be achieved, am I right?
I read the article about the algorithm but it is still not very clear which parameters is better to pin and which to change depending on results from every test performed.
How to set the optimal values of the parameters of the SNIC algorithm?
Thanks in advance
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You can follow the following steps:
ee.Algorithms.Image.Segmentation.SNIC(image, size, compactness, connectivity, neighborhoodSize, seeds)
  • ArgumentTypeDetails
  • image:ImageThe input image for clustering.
  • size:Integer, default: 5The superpixel seed location spacing, in pixels. If 'seeds' image is provided, no grid is produced.
  • compactness: Float, default: Compactness factor. Larger values cause clusters to be more compact (square). Setting this to 0 disables spatial distance weighting.
  • connectivity: Integer, default: 8Connectivity. Either 4 or 8.neighborhoodSizeInteger, default: nullTile
  • neighborhood size :(to avoid tile boundary artifacts). Defaults to 2 * size.
  • seeds: Image, default: nullIf provided, any non-zero valued pixels are used as seed locations. Pixels that touch (as specified by 'connectivity') are considered to belong to the same cluster.
  • (from the tutorial of Google Earth Engine)
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I m a biologist who is starting to pursue a career in Physics now! in particular, on the subject of radar. Can anyone interested in this subject guide me on a book list or articles regarding Radar basics
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As per recent media reports and apprehensions from airlines, the 5G services in the vicinity of airports might affect the landing and navigation of flights, especially due to C-band of 5G which is very close to altimeter frequency of RADAR. One of such media reports can be seen at: https://www.reuters.com/technology/exclusive-major-us-airline-ceos-urge-action-avoid-catastrophic-5g-flight-2022-01-17/
Does this apprehension stand valid?
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Since 5G spectrum is very near to aviation RF communications and slight deviation of antenna toward up may affect large height with strong RF signals. Coverage distance on Earth surface may be limited due to curvature and air but towards sky these RF waves could travel thousands of KM.
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Is there any script or tuturial that use indexes or other calcs for this purpose? I need find vegetation, urban areas, water bodies and bare soil in an S-1 image. Thank you, in advance.
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The single Sentinel-1 image provides not so good quality for Land use/land cover classification. Fusion of Sentinel-1 radar with optical Sentinel-2 images gives much better accuracy. The example of such approach, see in:
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I want to use radar photos to study the earthquake. Is there a way to know where to be a pair of interference photos?
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You can download sentinel 1 images from esa website. Then try to generate interference images in freely available SNAP software.
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As we know that FMCW radars often use two antennas to increase the transmit/receive isolation.
Assume we have stationary target at certain range , and The transmitted signal is LFM signal, when starting the transmission process , The baseband spectrum shows a single echo , firstly I thought that this echo is The target echo, but when I turned on The transmitter again With no targets , I found The same results as With a target (no changes in The spectrum) Why the receiver Only sense The transmitted Signal Without any echoes received and how i separate between The transmitted signal and The received echoes??
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I would recommend improving the low angle isolation between tx and rx antennas by using absorber and/or choke-rings (if your bandwidth is not too large) or some kind of barrier between antennas. The idea is to maximize the sensitivity of the receiver without adding clutter in the zone you want to probe with the radar. Close-in radar returns are removed by ignoring those FFT frequency terms (i.e. the low order ones) in the received signal.
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I need to present some data in a form of a Radar graph and I don't want to use excel and I need another software.
What is the best way of making this kind of chart that can be accepted academically?
Thank you for your help and software
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Please find attached two images.
On image shows the location of 5 polygons, two of them at the ocean, three at land.
The other image shows radar Sentinel-1 time-series data (in decibels) from 2014 to 2021. The plots show the mean and standard deviation into each polygon. In blue are plotted the ocean polygons and behave clearly the same way.
Some jumps can be easily seen both in std and mean values (specially in January 2020).
Why might it happen? Did something changed in the processing of sentinel 1 images?
thank you all in advance.
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That's interesting. I also encountered some issues like that where the processing of Sentinel-1 is not consistent in GEE. In my case, I think the terrain correction was not applied in some images for some reason:
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Hello everyone,
Can someone please let me know what does "ensemble average for a given window of pixels" mean when calculating a coherency or covariance matrix? I have taken this sentence from a paper "Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation".
According to my understanding I have to consider a window of size m x n and take the pixels of a radar image in that window and then perform ensemble averaging i.e. mean of those pixels. Please let me know whether my understanding is correct or not.
As of now I am performing a 2D convolution with a square window and varying their size to analyse the performance.
Thanks in advance.
Best regards
Pavan Kumar.
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Dears Pavan and Sriram,
I have just arrived now to the same question. In some papers it appears the next operations for sentinel 1:
r = { |VV||SVH|}
where |x| means the modulus and {x} means ensemble average.
If I am working with GRD sentinel 1 image. I would take the absolute value of each band -VH and VV respectively- but from that point I do not know what to do as averaging VH and VV pixel by pixel sounds a bit rare to me.
What am I misunderstanding?
Kind regards,
Jaime
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In FDTD there are two kinds of farfield results, namely antenna gain and scatterer RCS. So what's the difference between positive and negative RCS (in dB)? Is it related with energy absorption?
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The unit of measurement for RCS is square meters. The decibel is normalization. It may be the same as mentioned above, or it may be different.
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I have two .pcd files and I have extracted features using FPFH estimator. Now I want to compare these descriptors, would that be possible? if so, then how can i do that?
Any help would be appreciated!
Thank you.
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Convert your .pcd files to LAS and add your features as classification in your new LAS files. Use LAStools for this. Then use CloudCompare to read the LAS files. CloudCompare can then be used to compare your descriptors (Classification). CloudCompare is free and runs on several platform.
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I performed a test simulation with the WRF model by applying the Shuttle Radar Topography Mission (SRTM) 1-3 arcseconds data, compared with the United States Geographical Survey (USGS) data. I expected the simulation results to be better with SRTM. Unlike, I found the results to be better with USGS. I am wondering if I made a mistake during the model performance.
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I think USGS also provides the same SRTM data but yeah AsterDEM provides better quality than SRTM or USGS.
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Hi,
Are there any research on radar used for inspecting indoor wall, i.e detecting insulation material apart from just concrete and rebars?
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Hello Sriram Badri,
I am not aware of any research on the radar detection of wall insulation, although there is plenty published on radar imaging through walls. I would imagine that the detection / classification of the insulation used in a wall cavity would be very difficult for two main reasons. (1) most insulation material is very low density material with a large air content and hence its relative dielectric constant would be very close to 1. In this case, it would be very difficult to distinguish the insulation material from air (or free-space). The reflection coefficient of the wall / insulation boundary would be almost exactly the same as for a wall / air boundary making it exceedingly difficult to distinguish the two cases. (2) there would be a significant reflection from the wall surfaces and there could be high loss through the walls (depending on wall material and radar frequency). Walls with metal studding, rebar etc. may even form a 100% reflecting layer. The large wall reflections and transmission losses could well mask the subtle differences due to a cavity fill.
I do have considerable experience in radar and also in the analysis of layered dielectrics. I have made measurements on the dielectric properties of various wall materials, particularly indoor building materials where corridors may form waveguide-like structures. Please feel free to get in touch if I might be of further assistance.
Regards,
Clive
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I have the scattering matrix images (8 images: S11_real, S11_imaginary, similarly for S22, S12, S21) and I need to create the coherency matrix images (6 images: Diagonal and upper elements T11,T22,T33,T12,T13,T23). The sensor is mono-static so S12=S21. How can it be done using python\MATLAB. Kindly share the required library/code or equation, required for it.
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Hi,
You may use the following python code to create T3 from S2 matrix. Since, you have mentioned the sensor is monostatic, considering the reciprocity constraint S12 = S21, the code should produce the required output. Find the attached formulation and python file.
Good luck with polarimetry :-)
# -*- coding: utf-8 -*-
"""
Created on Mon May 3 09:00:21 2021
@author: Narayana
"""
import numpy as np
S11_real = 1
S11_imag = 0
S21_real = 0
S21_imag = 0
S22_real = 1
S22_imag = 0
# Scattering matrix
S2 = np.array([[S11_real+1j*S11_imag,S21_real+1j*S21_imag],
[S21_real+1j*S21_imag,S22_real+1j*S22_imag]])
# Kp- 3-D Pauli feature vector
Kp = np.expand_dims(np.array([S2[0,0]+S2[1,1], S2[0,0]-S2[1,1], S2[1,0]]),axis=1)
# 3x3 Pauli Coherency Matrix
T3 = np.matmul(Kp,np.conj(Kp).T)
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Should we perform radiometric calibration, multi looking etc. for the processing?
What pre-processing steps must be performed for further forming the coherency matrix?
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Thanks you Mounira Ouarzeddine for the answer, that clears it a lot.
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How can I download free radar images using archeology and explore radiographs a few centimeters deep?
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Depends. Contact source or primary researchers first. Most will likely send you for free. Institutions and centers often charge a fee.
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How many vegetation indices are there using radar images not multispectral bands?
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Dear all:
When dealing with historical single radar station data (default coords system is polar), when I convert the polar coords system into cartesian system (e.g. WGS84), there will be some NaN region left around four corners for converted data. How to deal with these NaN value regions by proper interpolating methods?
Thank you all.
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One option might be to give these cells with missing values the mean or median value of the surrounding neighborhood of cells; maybe an 8-neighbor rule.
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In physics, the description of the properties of light generally refers to its wavelength (nm), while in the range of microwaves and radio waves we refer to the frequency of a signal (e.g. kHz or MHz). Why is that so?
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This is just history, and how things are measured. It is also not true that microwaves are only referred to by frequency. They are called centimetre waves, and 100 GHz is referred to as mm wave. Even at low frequency, am radio at 200 kHz was known as long wave, and we looked for it at 1500 metres. Above that was short-wave! At low frequencies it is easier to measure the time of a period, or how many periods there are in a second. With light it is easier to measure the wavelength using diffraction by a grating (which has its line spacing measured in microns, or lines per cm), with a formula that relates angle to wavelength and grating period.
I am learning to think in both, because I am a microwave man, but am now working in near infra red, so am just learning what that is in frequency, but as I type this I find I still have to work it out and don't know wavelength and frequency instinctively yet, as I do from 1 GHz to 300 GHz.
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I've trying to access hourly precipitation data from NCEI's Radar Archive however I've not been able to configure past the downloads. Is there any code available to decode the data?
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Hi,
If you are familiar with R language, then I'd recommend rnoaa package that allows you to retrieve NEXRAD hourly prec. data with something as simple as
ncdc(datasetid = 'PRECIP_HLY', locationid = 'ZIP:28801', datatypeid = 'HPCP', startdate = '2013-10-01', enddate = '2013-12-01', limit = 5, token = "YOUR_TOKEN")
More information
Hope it helps
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I am currently working on the radar technology and am currious what is exactly the list of all possible informations a radar can deliver.
I know that it measures the time, intensity, and other characteristics of the energy that returns from targets and determins the range, angle, and velocity of this objects.
But is that it?
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The brilliant answer, dear https://www.researchgate.net/profile/Clive-Alabaster! As additional point I'd like to say that you can get estimation acceleration not only via velocity differentiation but also after Fast Fourie Transform of signals. Very important are angle accelerations of rotation line of site of targets for detecting maneuvers etc. as well. Using the superresolution technique, it is possible to obtain information about the number of targets in one impulse volume. On the other hand in multistatic radars with digital antenna arrays as information can be use big data massive of signals voltages from every radars to coherent signal processing and jammers location.
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Dear all, I'd like to open here a sort of forum for understanding how the geodesists community is moving in view of the X-band SAR satellite constellation. The new constellation will offer new "free, near real-time SAR data" with the "latest information about any spot on the planet within the hour". This will open completely new horizons for InSAR monitoring of ground deformation especially for rapid phenomena such as eruptions and seismic crises. The huge amount of so frequent data acquisitions will open also new needs for rapid and automatic processing. My question are: who knows more? Are you planning a routine use of these data? How?
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Wow, thank you. I'm looking for understanding their high-frequency repeated interferometry for ground deformation monitoring. Will data be open or should they be bought?
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If a radar send an electromagnetic wave to track some object I would expect that the returned wave (from the object) is accompanied by backscattering from atmosphere molecules. My question is, how is this atmospheric backscattering with respect to the transmitted wave?. The magnitude of the backscattering as well as the shape of the power spectrum concerns me. Thanks!
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Dr. Ariel Arza, for the case of SAR, there are interesting studies on the effects of backscattering.
It looks like the SAR technique records and enhances well backscattering images.
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Dear everyone,
I performed some experiements with a FMCW Radar Instrument emitting waves of 3.8mm. I can see some "movements" when I am looking on the relative displacements (+/- 0.15mm) over multiple stable points (corner reflectors). I assume part of this "displacements" is caused by temperature, humidity and pressure variation over the time of measurement (outdoor experiment). However, when applying Rüeger (2002, Refractive Index Formulae for Radio Waves) formula then the values are about a factor of 10x too small. There does not seem to be an error of units. The paper mentions resonance lines at about 67GHz and I am wondering if this could have an influence on the measurements with 79.5GHz and is there a publication specifically for the frequencies around 80 GHz?
Would anyone have a suggestion what else could cause this "movements". The radar and the targets are mounted on a concrete pillar and I assume that there is no relative displacements due to the similar setup.
Thank you very much and best regards,
Andreas
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Very interesting question
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I'm looking for a method to find the Doppler center of complex SAR image. Can anyone suggest the easiest way to estimate the Sentinel image Doppler center ?
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The Doppler center (line) depends on the S/C position, velocity in the Rotating Earth-fixed (ECEF) Coordinate system. So formally you would need orbital information. Luckily the orbital tube Sentinel-1 is controlled accurately, and provides a quite stable S/C position and velocity as function of Sentinel-1 Ascending Node (ECEF) crossing (time). This crossing time oscillate with magnitude of 0.3 sec and precesses some +7 seconds per year. So a previous estimate of the Doppler center for a particular UTC time (N times 10 days apart) might help to obtain a guestimate for the next Doppler line at UTC (0) + N x 10.0 days + N x 10/365.25 x 7 sec
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I want to extract RCS values or backscattering coefficients from available Radar imagery and create a Geospatial layer out of it.
Thank you.
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Gomal Amin Thank you for your response.
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I am quite familiar with the general concept of bistatic FMCW radar.  The issue that I was trying to understand relates to an HF bistatic FMCW radar and what techniques are available that allow detection of zero Doppler targets in the presence of the direct radiated signal. This radiated signal will results in a zero Doppler response at all ranges in the direction of the transmitter that in general will dominate and hence mask returns from these stationary targets if not removed.
I have searched but can not find a reference as to how this issue is addressed. 
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Thanks - appreciated. Do you have a paper that I can reference.
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Hello,
I am trying to optimize an antenna array for MIMO radar.
I want to start by using a MATLAB code to display the aperture and the PSF (point spread function) of the array.
Could anyone give me advice on where to start or suggest a Matlab tool for me?
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I am working on Radar Emitter Classifiers and I want test my codes. unfortunately I'm unable to find a dataset to test my algorithms. I'll be glad if someone can help me with this...
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This article has a lot of datasets and also information about the source of these datasets. it will help you
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My vision on the future of automotive radar! What is yours?
Future automotive radars will soon go back to analogue as it is the only way to overcome what are perceived as the limitations of conventional radars, in terms of reduction of both computational efforts and radio frontends complexity and mutual interference mitigation, by using agile waveforms based on pseudo-noise generation (analogue) electronic circuitry and metamaterial-based analogue beamforming. Ultimately, it will give room for artificial intelligence engines to be built into a complete radar package, leveraging radar solutions to the next level. This imposes stringent requirements to build an affordable, high performance analogue radar platform with the complexity and cost that one would see in military grade operations.
I am really pleased and honoured to have just delivered my keynote on "Future Automotive Radar Goes Back to Analogue" to a massive internet audience, promoted by #BOSCH PORTUGAL.
Please follow this link to watch my talk and do provide your feedback on the disruptive ideas for next radar generation: https://media.video.bosch.com/media/t/0_t5el3pb5 [in Portuguese]. This talk has addressed ongoing research topics by my research group towards the use of pseudo-noise based radar in real case scenarios, including a benchmark against well established commercial off-the-shelf (COTS) radar solutions. On the antenna beamforming technology, electronic tunable metamaterial-based solutions will be presented as a way to mitigate the extremely slow reaction speeds in fast time-varying scenarios using current market solutions and, thus, to create the opportunity to introduce artificial intelligence into all-encompassing radars platforms for target detection and identification in harsh environments. 
You may follow our recent publications on this disruptive radar topic. Cheers.
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An interesting forecast. But I think that digital antenna arrays will be more efficient in the next 10-20 years. In any case, I wish you and your company good results in realizing your concept.
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Given the I and Q channel data of the radar received signal, how to calculate the signal-to-noise ratio of the signal?
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In that case, measuring receiver noise as described by Vadym is the way to go. Be sure to account for any differences between receiver bandwidth and pulse echo bandwidth. Statistical properties of single pulse detection will be equivalent to ASK modulation, Perr = 1/2 erfc(sqrt(E/4N0))
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I am performing measurements with a ground-based scatterometer (or radar) to measure the backscattering coefficient (s0) of ground surfaces. My scatterometer basically consists of a vector network analyzer (VNA) and two dual polarized broadband gain horns, one for transmit (tx) and the other for receive (rx). The two VNA's transmitters are connected directly (I omit test port couplers) to the two input ports for the tx antenna (one for horizontal- the other for vertical polarization). The two VNA receivers are also connected directly to the rx antenna outputs (also h- and v pol.).
Now for my question. I want to determine the lowest possible signal I can measure with my scatterometer. Do I need to consider the gated sky measurement as my lowest possible signal as is done in for example [1], or is the lowest possible signal (still) simply the noise level of the receivers? Personally I think the latter is correct because you substract the gated sky measurement from any target target response. The gated sky measurement, as I see, it is simply a(known) offset. same time it still captures what part of the coupling is still contained in the time window associated with the target. When calculating s0, I substract the gated 'sky measurement' from the response I get from the target.
Now for my question. I want to determine the lowest possible signal I can measure with my scatterometer. Do I need to consider the gated sky measurement as my lowest possible signal as is done in for example [1], or is the lowest possible signal (still) simply the noise level of the receivers? Personally I think the latter is correct because you substract the gated sky measurement from any target response. The gated sky measurement, as I see, it is simply a(known) offset.
Any help would be appreciated.
[1] Nagarajan K., Liu P., et al., 2014, IEEE GRSL, Automated L-band radar system for sensing soil moisture at high temporal resolution.
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Thank you for your reply mr. Pavlov. Measuring long term drifts of the response when aiming the antennas upward was not possible unfortunately due to practical reasons. On the short term however (several hours) the sky measurement response did not change. Temperature variations in the VNA housing does indeed alter the measured response, as you mentioned. Other scatterometer/radar systems use a so-called internal calibration loop for this purpose. With a internal calibration loop the receiver is collected to the transmitter (with a switch) via a reference transmission line with pre-determined attenuation and phase length. Any measured changes with respect tot the per-determined response can be identified that way and compensated for. My system did not did not have such an internal calibration loop. To estimate the effect of varying temperature an experiment was performed in which the response of a fixed target was measured with different VNA housing temperatures. The observed variations in the response I then treated as a system uncertainty which I apply to the ground-target measurements. As for my original question on what to consider as the lowest possible measurable value, somebody else pointed me in the right direction. When transforming the sky measurement frequency response (via inverse discrete Fourier transform) to the time domain you get the response-over-distance (ROD). In the ROD you first see a peak that represents the antenna cross coupling. This peak decays over distance until it reaches a stable level. This stable level at the end of the ROD can be considered as the system's lowest possible measurable value. This method is also used in [2]. [2] Baldi C.A., 2014, UofMA MSc thesis, "The design, validation and analysis of surface based S-band and C-band polarimetric scatterometers"
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noise detection filters like median filter is not useful for random value impulse noise. How RVIN can be detected?
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The Adaptive Noise Detector is used to detect the type of noise such as Gaussian noise, salt and paper and so on, if exists in the current image.
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According to the attached paper, dated 1977, in the American Journal of Physics, by Professor P.D. Gupta of Purdue University, a Lorentz transformation analysis of the longitudinal Doppler shift in light, over a two-way path, is equivalent to two separate classical analyses.
Are we sure that we can definitely use the Lorentz transformation analysis over a one-way path?
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Dr. Jackson, Yes, Gupta's maths is correct, and that's all that matters at the moment. The implication is that the Lorentz transformation Doppler analysis over two paths is equivalent to two separate classical analyses.
If such an analysis only operates over a return path, then we won't have any of the conundrums associated with what the speed of light is measured relative to.
Your remedial theory about the light slowing down after the collision was designed to address a conundrum which may not exist.
So the title question is "do we have any experimental evidence confirming the LT Doppler shift analysis over a one-way path?"
That would mean experiments where the Doppler effect in light is measured first with the receiver moving relative to the source and then with the source moving relative to the receiver to see if the result is different. Have any experiments like this ever been performed to any degree of accuracy? It would have to involve very high terrestrial speeds in order to reveal any significant effect.
The fact that Gupta has shown the LT Doppler analysis over a two-way path to be equivalent to two separate classical analyses, is quite persuasive that maybe it only holds over a two-way path and that we have both been until now trying to solve a conundrum that does not exist.
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Hello,
My question is what is the best and the most accurate method to simulate Radar Cross Section for simple objects (circular flat plate, rectangular flat plate, ... etc) using ANSYS HFSS19, I found some methods in google but some of that do not make sense while you thinking about it.
So if you got any good method to get accurate total RCS vs Aspect angle results using HFSS, please put that as soon as possible.
Thank you all.
Best regards.
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You can check out this document from ANSYS on FEBI boundaries, it could be of interest to you:
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I am dealing with ' Optimal signal processing of frequency-stepped CW radar data' from the website of
I want to find the hidden defect in ceramic sample.
I will use the proposed method inside the paper to overcome the limitation of IFFT which is giving can giving correct number of peaks inside the graph after using the proposed method. However inside the paper, one of the procedure state that assume a set of t and n number of Ai and ti to be extracted
My question is
1.how to assume a set of t and n number of Ai and ti to be extracted?
I have a set of data in time domain with the number of 3 peaks and 101 points of data
2. Can introduce me other similar papers as well so that I can solve my problem in finding hidden defect in ceramic sample in matlab?
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The Phalanx CIWS is a computer-controlled cannon system. This system, which is deployed on many American warships, is designed at the flick of a switch to detect, track, engage, and confirm kills using self-contained radar. A human operator cannot match the performance of such a device. But the duration of its automaticity is regulated by a human operator. Will a day come when such automaticity is controlled by another automaton due to its superior performance? How many layers of automaticity should be tolerated when fighting a war? Some have suggested using block-chain computer code to better regulate autonomous systems (Husain 2017).
Reference
Husain A (2017) The Sentient Machine. The Coming Age of Artificial Intelligence. Simon & Schuster, New York.
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- if the question You formulated earlier is still relevant to You,
-- viewing the recommendation I paid attention to the counter arguments [@ Dr. Deborah Jean Verran ] in the same place and almost at the same time [ https://hbr.org/2018/06/what-blockchain-cant-do ],
--- in particular, the citations (by Catherine Tucker and Christian Catalini): "At the interface between the offline world and its digital representation, the usefulness of the technology still critically depends on trusted intermediaries to effectively bridge the “last mile” between a digital record and a physical individual, business, device, or event. In our example, the technology would have to rely on humans to correctly and honestly implement the match between baby*
{*comment: the authors there give a transparent example with a child}
and digital record. And if humans get that wrong or manipulate the data when it is entered, in a system where records are believed ex-post as having integrity, this can have serious negative consequences."
With best regards,
Alex.
P.S. My English may not be good enough, please correct if I made a misunderstanding.
P.P.S. Indeed [@ Dr. Deborah Jean Verran], It is interesting to watch all of this from the sidelines and see where it heads.
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I am wondering how to use FMCW radar to estimate ego-motion? Is there any publication focusing on this topic? If one radar on the vehicle is not enough for estimation, how about using both on the vehicle and road, utilizing the sensor redundancy to make up the weakness?
Why choose radar is because in contrast to cameras, lidars, GPS, and proprioceptive sensors, radars are affordable and efficient systems that operate well under variable weather and lighting conditions, require no external infrastructure, and detect long-range objects.
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FMCW radar with phased array antenna is suitable for your work. But will haven't your system high accuracy, and you have to use other methods such as LIDAR, GPS, etc together.
if you use FMCW radar and LIDAR together, the accuracy of your system increases significantly.
We implemented a novel technique in laser rangefinders and LIDAR systems, which is low-cost and has high accuracy.(this technique was tested and currently, we are writing the results for publishing.)
maybe this technique will be suitable for your work.
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I have been looking for open data bases on micro-Doppler for drones but I couldn't one. Does anyone know where I can find such a database for download?
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Drone based RADAR data is not available right now.
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Ionospheric radar it can generate 2D image forming technique my the linear rectangular array Ionosphere radar are recently established at Gadanki (13.5°N, 79.2°E; 6.5°N magnetic latitude) in this Radar is operated in the frequency range 30MHz operation in this are used to finding in the atmospheric layer characteristics coherently Radar imaging technique I have generates In phase and quarter phase IQ data of the Each range bin and each height of the radar and it takes the visibility matrix of each height and each time of NFFT points how is it generates an image of each receiver visibility matrix are to be multiplied are not to the weighing vector
plese give me the solution of this work this RAW DATA are 5 channel receiver database papers of the following are their
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thanks for information Dr. Oleg I. Berngardt
sir,
but in this are only particular region not in the layer of the ionospheric region of E layer echoes data are received in the 5 channel receiver header file data encrypted file is decrypted then generating in the waveforms of both IQ data but the antenna positions and baseline lengths are depending on the received vector of s(t) in the paper of RDplamer spherical coordinate system in our radar station is the not advanced in the ionospheric radar only for x and y-direction of the image generation cartesian coordinate system are used to find ing in the steering vector of the s(t) and direction vector of (W) and it find weight matrix of the antenna in the 5 channel data are their but resolution of the image are very low compare to our base paper in the after generating the IQ data processing the wave it generates image directly one file shows without Fast Fourier Transform, autocorrelation function, and capon algorithm image of the that layer are shown
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Some radars use several pulse-widths. I encountered a radar uses 2 kind of pulse-widths in turn.
I think 2 pulse-widths are used to solve ambiguity in Doppler and range.
What else are the reasons?
Do you know any more reason?
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Solving range and velocity ambiguities is often accomplished by comparing target data acquired over two, or more PRFs (not pulse widths). A burst of pulses at a fixed PRF may be transmitted and processed within one coherent processing interval (CPI), and then the radar shifts to another CPI on a new PRF; the process may use up between 5 to 9 PRFs in a medium PRF mode (8 PRFs is commonplace), or fewer in low and high PRF pulse Doppler modes. Changing the pulse width in sympathy with the change in PRF enables the radar to operate on the same duty ratio and this has several advantages for the radar. Firstly, a constant duty ratio maintains a constant average power and so the same energy per CPI (assuming equal number of pulses per CPI) and this, in turn, yields a constant detection performance over the various CPIs. Secondly, it may be advantageous to run a high power transmitter at a constant load and so maintain the same average power (if the pulse width didn't change with PRF, the load would vary between CPIs). This is more of a practical engineering point in designing transmitter power supplies. This is perhaps most likely to be the reason why a radar uses several pulse widths but there are other reasons. Changing pulse width to maintain a constant duty ratio brings about a few complications so not all radars will do this.
One of the other reasons for changing the pulse width may be that a shorter pulse is used at shorter ranges (and a longer pulse is used at longer ranges). The shorter pulse has less energy and so a reduced detection performance but this is acceptable at short ranges. Also, a narrower pulse width has a shorter blind range (and close-in eclipsing losses) and this may be important in order to maintain target visibility and minimise the effects of partial eclipsing at very short ranges.
There could be other reasons for using differing pulse widths but the two described above are perhaps the most likely. I could say more if you could tell me more about the radar and its waveforms.
Feel free to contact me if I can help further.
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What is the principle difference between RF RMS Power Detector, log Power Detector and RF Envelope detector?
What is their application difference and I want to detect a received signal envelope (Pulse Modulation) at 10 GHz which type of detector would work best?
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I am little bit worry. You said that you want to detect the signal envelope and then you speak from other types of detectors such as RMS detector and log power detector. The RMS detector can be accomplished by using square law devices such as the Mosfet transistors. The log power is only one take the log of the power from the mean square detector. This is called a power level scale.
Then remains the envelope detector which is built from a diode loaded by a capacitor and resistor. The output voltage at the RC parallel combinations follow the envelope of the RF signal.
One can process the envelope to regenerate the data pulses or the information.
Best wishes
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For radar with long pulse duration and low pulse repetition frequency, the velocity of the target changes between different PRT or even within the PRT, and it will affect the  correlation properties of the sequences.
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Your point is correct. This is a good but complex question. It can be divided into another project to research. Currently I wold like to consider the vertical part of transient speed of a flight target only, i.e., its velocity/Doppler frequency is constant at a moment.
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In some FMCW radar configurations, I & Q demodulator is used to dechirp the RADAR received signal. The difference between these 2 components is in phase, Q channel is shift by 90°.
I have a real adc raw data contain 512 samples ( I & Q channels), extracted from an Evaluation Development kit and I want to get target range & relative velocity.
Usually a Range-FFT is applied after a windowing function to estimate target range or at least to get beat frequencies and this is what I exactly did. I assume after the FFT processing I have to see some peaks (targets, and multipath and multipaths).
Atteched picture presents different plots I got:
Hanning s used as a windowing function.
Questions:
  1. Why there is 2 channels and we can work only with one IF channel?
  2. Do I need to perform any additional post processing algorithm before applying Range-FFT?
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averaging the time series data for N consecutive pulses(coherent integration) and then incoherent integration is used before the FFT or clutter removal technique is used and then apply FFT averaging in the power spectrum components
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Conventionally radar QPEs are evaluated considering gauge data as the ground truth. Is there any other reference data or other method that we could use?
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I am currently doing a master thesis research on improving the quality of radar QPEs too, that's why I go through this question even though a bit long time to share.
Radar rainfall estimate has strength with high spatial and temporal resolution, but still a concern with several errors and uncertainties like beam blockage....etc. So to obtain the high accuracy and high-resolution rainfall product, we have to merge or adjust it against rain gauge which is known as the most accurate point estimate rainfall.
Answer to your question, I have read a thesis study by Malte Kristian (2015) with the title " Adjustment of rainfall estimates from weather radars using in-situ stormwater drainage sensors". In his study, he simplified the traditional method you have mentioned by introducing the method of Radar-runoff adjustment rather than radar-rain gauge bias adjustment.
Refer to the picture attached,
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Autonomous Driving:
We just get from the sensors (Lidar, Radar, Video) the position of the other objects (x, y) and directly (Radar x´) and indirectly (LAser, Video by differentiating y we get y´).
How can we get x´´ and y´´, which means the prediction of the trajectory of the other objects?
1.) We can extrapolate x´and y´ by Kalman filters.
2.) We can make a driving dynamics prediction by taking Kamm´s circle.
3.) We can make a prediction based on the environment like curves and stop signs, traffic lights.
4.) We make an ethical prediction by estimating how the other driver/pedestrian/cyclist behaves to not cause an accident.
Especially to points 3 and 4 I would like to get bettern known the state of the art. Who knows the relevant research in this field?
Thanks
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With any predictive model, human behavior is tricky and one cannot really, that I know of, predict it with any high degree of certitude. To start, you would need an enormous volume of watched data, be it driver or pedestrian movements.
That caution said, we are in the business of data gathering and observations, so who better than a seasoned traffic engineer to make learned predictions and then to inform ourselves or audiences that these are our best prognostications of how vehicles or people move.
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Hi everyone
is there any practical dataset of human body radar micro-doppler signatures for download?
for example a radar version of mocap dataset of CMU?
I really seeking for such a dataset. :-(
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How can we do it together? do you have any Idea?
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the difference between these two products.
Level-1 GRD consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at the cost of reduced geometric resolution. Ground range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected magnitude. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle, but with reduced resolution.
Level-1 SLC products consist of focused SAR data geo-referenced using orbit and attitude data from the satellite and provided in Zero-Doppler slant-range geometry and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in Zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track. The products include a single look in each dimension using the full TX signal bandwidth and consist of complex samples preserving the phase information.
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IW is GRD. Thanks
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I've been looking for any standard to evaluate radars capability in electronic warfare. In other words if you buy a radar is there any standard to test and evaluate its ECCM capability?
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Due to the sensitive nature of the topic, one would struggle to find an open standard to assess against. However, there are many figures of merit which can be used for comparative analysis between systems. The ECCM improvement factor (EIF) proposed by Johnston would be a good starting point. A discussion between Johnston and Li regarding their respective work in the area, with many useful references, can be found at https://digital-library.theiet.org/content/journals/10.1049/ip-f-1.1985.0044
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SENTINEL-1 IW Level-1 products are Single Look Complex (SLC) and Ground Range Detected (GRD).
Which one is better for Land Cover Classification?
Below is the difference between these two products.
Level-1 GRD consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at the cost of reduced geometric resolution. Ground range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected magnitude. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle, but with reduced resolution.
Level-1 SLC products consist of focused SAR data geo-referenced using orbit and attitude data from the satellite and provided in Zero-Doppler slant-range geometry and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in Zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track. The products include a single look in each dimension using the full TX signal bandwidth and consist of complex samples preserving the phase information.
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I use SNAP, QGIS, ENVI softwares, and Python programming language for image processing.
Not too slow. Suitable for image processing tasks. But I prefer to use the Python programming language.
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I am working with a synthetic aperture radar system with fmcw signal, which transmits and receives signals continuously. The received signals are dechirped and their type is double (not complex). I want to separate the received signal of each pulse and prepare it for the range and cross-range compression.
In some instances, I've seen that the Hilbert transform is implemented on the signals to generate analytical complex signal, but I don't know its main reason and in many cases, it doesn't work appropriately!
I attached part of the received and transmitted signals.
I appreciate your comments in advance.
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Samples can conventiently be held in a 2D array of samples within each FM sweep (rows) vs samples from successive sweeps (columns). You will first want to focus the array by making phase offsets on samples as a function of their range and location within the synthetic array. Then a 2D FFT process of data will yield the cross-range vs range map. The FFT of the slow-time samples from successive sweeps gives the Doppler shift of a point which is a function of its cross-range location. The FFT of the fast-time samples within any given sweep gives the beat frequency which is a function of its range. This will get you a basic image/map.
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The radar system that I'm working with contains a linear FMCW S-band (2.26-2.59 GHz) signal with a bandwidth of 330MHz and a pulse duration of 20ms. Also, the received signal is dechirped.
Thanks for your comments and suggestions in advance.
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At least 44 samples. Given the power of DSPs these days, be safe and go for significant oversampling.
The useful range will depend on the transmit signal strength (Tx EIRP - includes antenna gain), the target radar cross-section and the receiver sensitivity (noise figure, LO phase noise, etc, Rx antenna gain). Google "radar range equation" and have a read. For good detection, you will need about 10dB received signal to noise ratio or more in the return signal. Use the radar range equation to estimate this and base your receiver bandwidth accordingly. Consider using a range amplitude correcting highpass filter (f^2 slope to correct for amplitude reduction for far-away targets) as well.
Cheers and have fun
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How to plot antenna radiation pattern with MS Excel, I have tried with Radar and got the shape but unable to change the angle step (angle value/range)?
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Dear Vijay,
Adding to the colleague Aparna you need only to google with the key word: Polar plot with Excel you get the answer demonstrated.
Best wishes
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When calculating interferometric coherence, why can't you do so on a pixel by pixel basis? I know the equation for estimating coherence = (|S1∙S2* |)/√(S1∙S1*∙S2∙S2*) where S1 and S2 are the two single look complexes. And I know this calculation uses a maximum likelihood estimator but why do you need to specify an estimation window and why cant the estimation window size be 1?
Thank you.
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You are absolutely right. Compared to optical remote sensing where 'adjacency' is a high order effect the signal impact of 'neighbors' is much higher here. A perfect coherence estimator would include dipol distribution, local 3D geometry for ray tracing and radiosity estimation and a gaussian shaped weighting window to 'reflect' the mixing and superimposition of the representing physical process. I was often thinking to compile a paper about all this in connection with a multi stage, alternative phase unwrapping.... Hope it helps. If not you can send me an email: rogass@gfz-potsdam.de
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There exist some basic models for the angle dependence of sigma0. In R.E. Clapp, 1946, “A theoretical and experimental study of radar ground return” three such models are presented:
[1] sigma0(theta) = constant * cos(theta)2 called: “Lambert’s law”
[2] sigma0(theta) = constant
[3] sigma0(theta) = constant * cos(theta)
[3] is actually more complicated since it can also include multiple reflections from deeper layers of the surface. If however one only considers direct reflections the model takes the form as shown above.
In Ulaby, Moore, Fung, 1982, “Microwave Remote Sensing, Active and Passive” vol. II the authors also discuss these models of Clapp.
With models [1] and [3] one cosine(theta) term accounts for the decrease in incident power per unit surface area when the radar measures the ground return under angle theta. With [1] a seccond cosine(theta) term is added in accordance with Lambert’s law: a radiating surface whose angle-dependent emission is according to I = I0 * cos(theta) [Wm-2].
The well-known integral form of the radar equation applied to surface returns is (see for example Ulaby1982):
Prx = Ptx * [ lambda2 / (4 pi)3 ] * integral[ G2 / R4 * sigma0(theta) , dA ]
What I don’t understand is why there is not a cosine term in this equation by default? So
Prx = Ptx * [ lambda2 / (4 pi)3 ] * integral[ G2 / R4 * sigma0(theta) * cos(theta) , dA ]
Because the way I see it: regardless of the scattering properties of any surface the incident power per unit surface area must be rescaled according to cos(theta).
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Thank you Jan Hofste ! That makes sense to me.
If you wouldn't mind sending Clapp's report, I would appreciate it. The data I'm looking at seem to vary according to [3], and I'd like to read more about it.
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Hi.I'm a student of communications engineering. I've been working on airborne radar target tracking methods.Is there any book you suggest me to study? Actually I found some books but I look for one to specifically study about "Airborne radar tracking methods"
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You can go through:
RADAR HANDBOOK
Editor in Chief : MERRILL I. SKOLNIK
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I wish to collect images of vehicle from radar (using radar imaging) I can see alot of journals talking about mathematics or theory of it but noonr gives practical tutorials on it ...can someone help me obtain radar images ?
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see Conference works
[IEEE 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI) - Sanya, China (2010.10.23-2010.10.24)] 2010 International Conference on Artificial Intelligence and Computational Intelligence - Inverse Synthetic Aperture Radar Imaging at Low Signal-to-noise Ratio
Ju, Yanwei, Yu, Li, Wang, Yang, Chu, Xiaobin
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When measuring the radar cross section (RCS) of a target the incident wave at the target must be planar or close to planar. As explained in for example: Kouyoumjian R.G. and Peters L. jr., 1965, "Range Requirements in Radar Cross-Section Measurements", a maximum deviation of pi/8 from a planar phase for the phase front over the extend of the target is still permittable. Using this pi/8 -criteria for a target with maximum dimmension L one can find the well-known formula R_min = 2 x L^2 / lambda, as is also explaned in aforementioned paper.
However, with some other publications, see for example Geldsetzer T., Mead J.B. e.a., 2007, "Surface-based polarimetric C-band scatterometer for field measurements of sea ice", the antenna's maximum apperture dimension D is used instead in the same equation R_min = 2 x D^2 / lambda to calculate the far field.
Is there a reason why one would use the antenna apeture dimmension D instead of that of the target L?
I am gratefull for your comments/answers,
Jan Hofste
University of Twente (Netherlands)
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