Global Navigation Satellite System - Science topic

furthermore, one a satellite is locked for recording the phases, a counter starts, keep add "1" to the number in the counter whenever a whole cycle carrier wave is passed based on the time a wave length corresponds to. So, the unknow ambiguity of a satellite maintains constant (associated with the time instant when the lock starts) as long as this satellite is being locked without interruption. In case an interruption, a different ambiguity will have to be resolved in the estimation.

I hope that there is something from them that can help you, and if you do not find it, just type the basic keyword in Google Scholar and you will find many articles about it.

Thank you very much professor Wolfgang R. Dick

The precision of a class must be defined by specifications, which define the RMS and the instruments to be used. If there are no specifications, then people involved in such a class get together and decide about the specifications for the class.

The number of digits also characterizes the precision of an instrument. If a theodolite measures an angle with a direct reading of one second, its precision is one second. If you want to test it you measure several times the three angles of a triangle, and you see how much the closing error is.

In any case, you define precision by specifications; you test precision by statistical analysis of measurements of a well-designed experiment.

Although I have some experience in all sorts of data wrangling, I have never processed COSMIC-2 files themselves.

Shaolin Lü,yes the dataset is open.I had analyzed the high-rated GPS data from SANT GPS station of Chile, with respect to the 57th, 58th, and 59th day (Julian days) of 2010, which was related to the great Chile Earthquake 2010.

Yes, without GCPs and RTK/PPK, it is highly possible to obtain wrong or inaccurate geometric information in UAV-based photogrammetric mapping. When dealing with large areas, relying solely on the drone's GNSS information can lead to distortions in the 3D model or orthomosaic. GCPs not only add global georeferenced accuracy but also help decrease errors in the scale of the results, making them essential for reliable measurements.

The accuracy of UAS-based photogrammetric mapping depends on several factors, including Ground Control Points (GCPs), flight height, camera resolution, GNSS accuracy of the device, weather conditions, processing software, user experience, etc. While it is possible to obtain satisfactory 3D models without GCPs or RTK/PPK, for precise measurements such as surface area or volume calculations, GCPs are essential. For more in-depth information, you can refer to my MSc thesis and the following articles. In the following studies, you can find comparisons of processing with and without GCPs as well.

MSc Thesis:

Good luck on your journey of exploration and innovation!

Hi Shanker,

You can get those from individual Analysis Centers (ACs) at CDDIS, while their GPS products are generally then combined into a single IGS product.

Attached here an example (multi-GNSS orbits & clocks):

Folder [e.g. GPS Week 2259]

> https://cddis.nasa.gov/archive/gnss/products/2259/

File [e.g. CODE MGEX Final product for DOY 115 in 2023]

> COD0MGXFIN_20231150000_01D_05M_ORB.SP3.gz

ps. MGEX = Multi-GNSS Experiment (https://igs.org/mgex/)

Regards,

Lotfi.

I think it is possible because the ambiguity fixed time is roughly around 30min. And if no cyclic slip occurs, the 1h data could be available for preliminary analysis.

Thank you very much @mustafa kamil

In order to have an accurate position solution the ionospheric effect need to be mitigated; different approaches can be used depending on the receiver characteristics. In particular, ionospheric error can be reduced using multi-frequency measurements, exploiting corrections provided by an augmentation system, relying on a model such as Klobuchar, NeQUick, NTCM. In the first approach, the dispersive nature of the ionosphere is exploited: the ionospheric effect is mitigated using a linear combination of measurements from two frequencies. Although, this approach allows to remove up to 99% of the ionospheric error it requires the use of a multi-frequency receiver that is in general more expensive than a single frequency device; in addition, the combination of the measurements lead to an increased measurement noise. Single frequency devices could limit the ionospheric error using an augmentation system like European Geostationary Navigation Overlay Service (EGNOS) or Wide Area Augmentation System (WAAS) ; these systems broadcast ionospheric correction for specific coverage areas. With this approach, a receiver needs access the correction through an internet connection or processing the Satellite Based Augmentation System (SBAS) signals adding complexity to the device and to the processing chain. Single frequency stand-alone devices need to mitigate the ionospheric error using a model. Recently, an empirical model Neustrelitz Total Electron Content Model for Galileo (NTCM G) has been proposed as an alternative to Klobuchar and NeQuick-G (currently adopted by GPS and Galileo, respectively). The three models exploits different approaches and have different characteristics: Klobuchar model is quite simple to implement and it is able to correct up to 50% of the ionospheric error; some advantages have been demonstrated when using the NeQuick-G model, which is able to correct up to 70% of the ionospheric error but it has an higher computation load.

So the accuracy with which you can estimate the iono delay is function of the strategy adopted and the model used. Finally, in case of disturbances the discrepancy between the estimation and the current iono delay could be very large.

Thank You !! Shouxing Qu Aparna Sathya Murthy !!

I suppose that the interferometric signal is available as a train of equidistant samples. In such case, the most flexible and also precise method for the frequency analysis is the discrete Fourier transform (DFT), the most common algorithm is the fast Fourier transform (FFT). Its output is a discrete frequency spectrum ranging from 0 to the Nyquist frequency, i.e. the half of the sampling rate f_s (f_Nyquist = f_s/2). The frequency spectrum is discrete, the frequency steps have the magnitude of delta_f = 1/T, where T is the length of the acquired data set in seconds.

As Jim Prater wrote in his post, the acquired data set can be observed as a multiplication of the original data multiplied by a square window with a width equal to T. In the frequency domain, you will see a convolution of the original frequency spectrum and the Fourier transform of the square window. The latter one is the sinc function, i.e. a sharp frequency peak (a delta function) in the original spectrum will be converted to a sinc function. The width of such peak is about 2-3 x delta_f, this implies that just by looking at the spectral data, you can determine the signal frequency with a precision of delta_f.

If you have acquired just a few signal periods, the precision is not exciting. If the signal contains, for instance, 3 periods, the frequency spectrum will contain a peak with the maximum at the index of 3, i.e. the frequency is (3±0.5) x delta_f. People usually fit the sinc function to the spectral data and obtain the real frequency with a better precision than ±0.5 x delta_f. There is, however, a much more elegant method that can be used here, it is called zero filling. You simply append zeros to the original data and increase the length of the data set in this way before you run the FFT algorithm. It increases the frequency resolution and improves the precision significantly. The peak does not contain of a few points more so that one can easily find its maximum. One can even fit a parabolic function to the peak around its maximum to further enhance the resolution. What improvement can be reached in practice is dependent on the noise in the acquired signal. If the noise is weak, one can improve the frequency resolution by many orders of magnitude. To get the answer you want, one has to simulate it or use the real data and just look for the results.

There is one point more that has to be mentioned: The sinc function is not the best peak shape that one can imagine. It has a lot of side bands, i.e. a single peak in the original spectrum results in many peaks in the calculated data. The side bands can obscure real peaks in the original spectrum, but if just a single peak is expected, they should not matter much. To improve the peak shape if complex spectra have to be analyzed, dedicated apodization or window functions were developed. One multiplies the data with such function before the FFT is calculated, i.e. one replaces the default square window by another function. One of the best ones is the Blackman-Harris polynomial with 4 terms, its side bands are about 5 orders of magnitude weaker than the main peak, i.e. under normal conditions, they are below the noise floor and therefore, not more visible in the resulting spectrum. Since the apodization generally increases the peak width, it must be carefully chosen since it can decrease the frequency resolution or obscure double peaks.

There are also much simpler methods that can be used for frequency determination but they cannot reach the precision of the Fourier transform. One of the methods is often used by frequency counters, where the time points are analyzed where the signal crosses the zero line or another other level. One can fit a linear function to them and obtain the frequency from the line slope. Since such method uses just a few data points from the whole data set, it is much more sensitive to noise and cannot reach the precision of the FFT which uses all available data. The main drawback of the FFT analysis is its complexity, it requires a lot of computational power and data memory - especially if data sets containing millions of samples have to be converted. For shorter data sets, the required resources are usually not critical and the results are excellent.

Yes. Thank you very much Junsheng Ding .

Shanker KC The Vienna Mapping Function (VMF) is a mathematical model that represents the mapping between the zenith hydrostatic delay and the entire zenith delay of the Earth's atmosphere (zenith hydrostatic delay plus zenith wet delay).

The geographical location of the site (latitude, longitude, and elevation above sea level) and the day of the year are fed into the VMF.

The overall zenith delay of the atmosphere at the location, including the zenith hydrostatic delay and the zenith wet delay, is the output of the VMF. Subtract the zenith hydrostatic delay from the overall zenith delay to obtain the tropospheric delay from the VMF output.

The VMF is commonly used in geodetic and geophysical applications such as GPS location and satellite altimetry to compensate for the effects of the Earth's atmosphere on distance and height measurements.

Because the ionospheric delays (and extra pseudo ranges or phases) differ at different frequencies, but the true distances are the same, by combining two equations you could exclude one variable. See "Eliminating the effect of the TEC" in Hofmann-Wellenhof et al. (2008).

Dear Yağızalp Okur ,

Here is an article:

Perhaps onto base of formula used by article writer you can create a formula useful for you... same see the critics of the forecasting method:

You really have to pay attention to the geological structure of the zone... as is written in the article

Regards,

Laszlo

#### Basic Approach

When setting the path to the PPP datasets:

-obs

-brdm

-sp3

-clk

you may use any of the following parsers (these are general keywords):

##########################################################

%Y ==> yyyy : four-digit year (2000-2099)

%y ==> yy : two-digit year (00-99)

%m ==> mm : month (01-12)

%d ==> dd : day of month (01-31)

%h ==> hh : hours (00-23)

%H ==> a : hour code (a-x)

%M ==> mm : minutes (00-59)

%n ==> ddd : day of year (DOY, 001-366)

%W ==> wwww : gps week (0001-9999)

%D ==> d : day of gps week (0-6)

%N ==> d : sequence number (0- )

%s ==> ssss : station name (lower-case)

%S ==> SSSS : station name (upper-case)

%r ==> rrrr : station name

##########################################################

For example, for your task, you may set the DOY for multi-day processing or the station name for multi-station processing.

Note: Remember to list the station names in the processing

scheme.

For example:

-karo

-mzuz

-ctpm

-vwzm

-...

#### Advanced Approach

You may write a program to call the following:

-PPP configuration file

-rnx2rtkp

The processing program may be written in

-Bash

-Python

-Julia

-...

Thank you so much for your kind responses. I have completed my Masters thesis and got an A grade on my thesis too. :)

The major difference is how to handle the position parameters in the filter. In PPP-Fixed mode, the position parameters are constrainted to the priori coordinates with a rather small variance, so its estimates almost does not change no matter the observations good or bad. PPP-Static mode consider the position parameter as a random random constant parameter, whose variance-covariance will decrease as the observation accumulation. Hence its position estimates will convergent to a certain value since the contribution of the new observations decrease. PPP Kinematic mode consider the position parameter as random variables, whose variance-covariance will be re-initialized every epoch. Hence its estimates will be noiser than the other two mode, but it is necessary for processing kinematic data unless you have other information like IMU observations.

Abdelrahim Ruby

You might be biased against measurements because you feel you cannot afford them. But often there are meteorological networks nearby - based on stations, weather radar, lidar, GPS dual frequency electron density, meteorological satellites, and many amateur networks. One measurement is sometimes worth 10 supercomputer's. You did not say what you are trying to do, or where. The location matters a lot - for the complexity of the modeled region, for the sparsity or abundance of continuous data stream, and for the possibility of others working on the same place.

I found that many of the seismic stations are recording meteorological data. Because when you work at parts per million or parts per billion problems the atmospheric variations are often one of the largest variations. Sometimes simple correlations remove or identify the the big variations. But I found that imaging arrays ultimately are needed.

I am sorry I cannot answer you more clearly. You don't say your restraints in terms of computing resources. If you have 10 supercomputer nodes, there are one set of options. If you have only a Raspberry Pi, then something else is needed.

I am sitting here listening to a lightning storm passing over the area. It reminds me of all the electromagnetic sensor methods for monitoring weather in 3D and quantitatively. You did not say how long your project related to the question might be. If it is a casual "I wonder?" or "a ten year plan", the methods are different.

I see you are working with geopotential. The daily variations of the gradient of the potential are easily measurable. After subtracting the sun moon portion of that, the largest component is the local atmosphere. I mentioned the seismic stations measuring temperature, pressure, humidity, acoustic. That is because the acceleration is correlated with atmospheric events and properties.

The most cost effective way to get good estimates for different locations is to use a combination of all the available continuous data streams and their correlations, then use algorithms that can run on low cost equipment. But all that is set by what you are trying to accomplish.

Atomic clocks are "direct geopotential sensors". You might want to look more closely at gravimeters and gravity gradiometers. Again, I cannot tell what you are trying to do, so I cannot be more specific. But generally the gravimeter networks are more sensitive to meteorological changes. I am trying to set up gravitational imaging arrays to 3D image the atmosphere for cities that also have 3D weather radar and climate models at sufficient resolution - for correlations. The less expensive gravitational sensors, once calibrated, are not bound by electromagnetic noise or weather conditions as much.

You seem to be assuming "I can get some data more easily by running a model". But often the best models are proprietary or horribly difficult to run. Then your best bet is to locate places on the Internet where the data is served continuously. The European Space Agency has a rather large project on global climate change that has to estimate global climate at any location over long time periods. Their model outputs are available. They keep moving things around, but you might try looking here - https://spacedata.copernicus.eu/ just to get some sense of what is going on. Brian Bramanto link is good. But the global climate model community is large and varied, and has many applications.

All their models, and any model you find will have to have been calibrated using real data, to be of any use. So whether you tap the raw data, or they do it and feed you the processed results - you are ultimately looking at measurements by someone. Usually many someones.

Looks like you and I are in the same project. You might want to take a closer look at Mossbauer methods for measuring the geopotential. That is the first absolute gravitational potential measurement. Also, the neutrino and cosmic ray networks are getting to the level of sensitivity and temporal resolution they can pick up daily and hourly variations driven by local pressure temperature humidity. Their results depend on the local atmospheric density.

Rather than TPH, you might want to think more in terms of mass density variations. PV = nRT is really very useful. n(i) = ParticlesPerCubicMeter(i) = MassDensity * AvagadrosNumber/ MolecularWeight(i) for sets of molecular and atomic species. i is the index for the species. It might seem trivial, but if you have to use it a million times and it gets embedded in thousands of different models, it is good to bring it out explicitly to be able to combine results from many groups.

Richard Collins, The Internet Foundation

Nequick topside model

Thank you for your answer. The programme works smoothly, even in the combination of GNSS and other sensors. It is also available for iOS and Android. It still has one weakness. GPS/GNSS/Location alone provides the number of satellites, in combination with other sensors this number of satellites is missing. Nevertheless, I will use this app.

Varsha J Lal

I suggest to read these articles:

1.

2. https://geomundus.org/2018/docs/papers/Amrit.pdf

Depends on what kind of phone you have. Many Android (older) Phones use U.FL connectors (https://en.wikipedia.org/wiki/Hirose_U.FL) . You can just disconnect the original antenna and replace it with the new one. On new ones you might have to solder a coax cable onto the PCB.

In the ITRF2014, the vertical (height) components of coordinates are slightly better for GNSS than for SLR: ca. 5 or 6 mm vs. 8 mm WRMS averages, see Table 2 in Altamimi et al., ITRF2014: A new release of the International Terrestrial Reference Frame modeling nonlinear station motions, 2016. (The full text is freely available at http://onlinelibrary.wiley.com/doi/10.1002/2016JB013098/full and was also reproduced in IERS Technical No. 38 "Analysis and results of ITRF2014", available for download at https://www.iers.org/TN38.) However, these are mean values for permanent stations and long measurements. A short measurement of a single GNSS station will be considerably worse.

thanks Endeshaw for your information

Thank you very much

This free eBook (100 pages) may be helpful for general questions on GNSS observations: http://arf.berkeley.edu/files/attachments/equipment/NovAtel-Intro-to-GNSS2015.pdf

You can read this paper:

Testing GPS L5 Tracking Algorithms and their Impact on Positioning Accuracy

Dear Dr. Maurizio Scaramuzza

Thank you for the reply. Please excuse me the delay with response. I found your answer very helpful. I'm going to implement your code into my calculation. I will let you know about the result. Best regards, PSD

Dear Alex Minetto ,

we worked on the design and implementation of of a GPS receiver for the sake of extracting the one PPS, we could realize the signal acquisition phase and the tracking phase where we could generate a copy of the carrier with reduced frequency and of the pn conde clock generator. BY dividing these signal with appropriate division ratio one can get the one PPS. Its stability was to be evaluated. But the last divider stage is not yet realized and we plan to realize it and after that evaluate its accuracy and stability. please see the papers:

Once achieved I will notify you.

Best wishes

It is working. https://arplsrv.ictp.it/

Convert the binary number to decimal and multiply with the scaling factor to get the proper value.

If the data is in 2's complement format, then Abdul Malik Khan Abdul Malik Khan convert q number to decimal and multiply the scaling factor.

There are a lot of websites with GNSS data,

It is better to select periods with good continuous data sets

sincerely

Christine

In my opinion, it's better to distinguish the study into disturbed and quiet period to validate the performance of the model. This is because, the average value of the entire month sometime fails to address the storm effect on the vTEC. By dividing the study into two separate categories, we will have better overview of their overall performance.

The explanation of your question here may not be enough, but I have had studies in this field and I suggest the following articles to you. I hope they help.

Wishing you the best on your way.

If you can send me some relevant GNSS monitoring data , you will be gratefully acknowledged in my future publications.

As soon as the receiver identifies the satellites using Doppler values, then that will no longer be the cold start but rather the warm start. At this stage, the receiver uses the recent almanac in memory to map the satellites and their Doppler offsets. Thus, what is necessary is only to check the anticipated pseudorandom noise (PRN) codes at the expected Doppler offsets. Henceforth, searching the sky becomes worthless.

How can we determine the satellite during cold start?

++++++++++++++++++++

Other than using the Doppler values, the positions of the other satellites may be detected using the orbital data from the almanac. In any given constellation, each satellite transmits orbital parameters for all satellites alongside auxiliary health and status information. Since the ephemeris data is transmitted only for each individual satellite, then you may simply compute the satellite positions using the orbital equations at any given point in time.

++++++++++++++++++++

Apart from that, you may get the ephemeris and clock information from the cellphone link rather than through the satellite link, a concept which may be termed as assisted satellite positioning. The strength of this approach is that the amount of time required to get all the critical information shortened.

++++++++++++++++++++

While the orbit design has something to do with the overall satellite configuration, the receiver architecture has a vital role in the task of identifying the satellites in order to calculate its own 3D position and clock offset, on the other hand. Therefore, the overall cold start searching time may improve with multi-corellator and high-sensitivity Global Navigation Satellite System (GNSS) receivers.

Well, 'Yes'. And not just the obvious spatial difficulties, it is the reliance on timing signals. The epicenter is NIST: Overview at https://www.nist.gov/news-events/news/2021/02/nist-finalizes-cybersecurity-guidance-positioning-navigation-and-timing and from there are other domain specific documents like https://www.nist.gov/news-events/news/2019/08/situational-awareness-electric-utilities-nist-publishes-cybersecurity

Behind all these is usually quite a paper trail of conference presentations, meeting transcripts, etc. from stakeholders providing input, usually referenced in the bibliographies of the documents. The exceptions are, obviously, classified content with specifics on actual incidents and unresolved current vulnerabilities.

When they first sent up GPS satellites, they programmed them with what they believed to be the correct relativistic changes that would be needed which would affect GPS location determinations. They also installed a programmable system within these satellites that can be reprogrammed from the ground, and they could upload any changes when the satellite was overhead.

What they found was that the relativity program the system was given was not accurate enough for GPS system calculations. They tried to correct these equations and related program for a period of time but finally gave up. Instead they uploaded a programmed algorithm that they believed could make the necessary corrections based upon information from the ground coming from one or more additional GPS satellites. The new system worked. So now all relativity corrections of GPS satellites are now based upon a self-correcting algorithm using new information from the ground and other GPS satellites on an ongoing basis.

Dear Hussein Saad ,

I think in order to cancel the intercept signal you have to detect it and that what you want to realize by your software defined radio. So need a software defined radio developing kit that work in the frequency band of the global position systems. You may use the national instrument software defined radio kits which are based on Ettus kits.

For more details please refer to the link:https://www.ni.com/en-lb/shop/hardware/products/software-defined-radio-device-bundles.html

Here I would like to pay your attention that the GPS signals are spread spectrum signals which are primarily more immune against narrow band jamming.

So, may not need extra precautions against the interference.

One must study how far these signals are immune against jamming.

I would like that you refer to the paper in the link which deals with generations

of such signals:

and

Best wishes

Based on my experiences on implementing NHC on real products, there are some challenges:

1 - Misalignment between vehicle body frame and IMU frame may be estimated, you can detect it when plotting the signals in the rear-wheel frame.

2 - If the vehicle turns drastically or jumps, NHC should be deactive.

3 - The measurement noise covariance matrix for NHC should be adaptively tuned.

You can have a look at Groves's book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems.

Depends on your study area. Local bodies and organisations sometimes observe these values and most often they are happy to share it for research work.

For such kind of implementations, a CORS network with RTK may probably works better.

Perhaps you could consider the IGS MGEX products. This RINEX files mostly contain data from all GNSSs.

Good luck!

It depends on multiple factors and some of which dominate only specific altitude which unfortunately make it very complex do not forget the geometry as well but this graph should be a good approximation.

There is no need to transform the ITRF2014 coordinates. "ITRF20014, ITRF2008 and WGS84 (G1674) are likely to agree at the centimeter level", see https://confluence.qps.nl/qinsy/latest/en/international-terrestrial-reference-frame-2014-itrf2014-182618383.html .

For automatic lineament extraction, you can achieve this by applying a combination of the following softwares: Arcmap 10.0+, ENVI 5.1, PCI GEOMATICA 2016, ROCKWORKS 16.

STEP1: Use ENVI software to perform principal component analysis (PCA) on the spectral image band of Landsat8.

STEP2: GEOMATICA is used to perform an automatic lineament delineation from the PC1.tiff generated from ENVI. This is done with the aid of lineament extraction algorithm in the Tool from toolbar

STEP3: ARCMAP is used for handling extracted lineament (splitting compound line into simple lines, editing lineament attribute, exporting lineament as cad file

STEP4: ROCKWORKS is used to process the exported lineament Cad file to determine the trend of the lineament and hence generating rose diagram.

The improvement is mainly because of large number of GNSS satellites above the receiver (GNSS) at any given point of time. This primarily improve the error adjustments and thus increase the accuracy. However, the issues under canopy still remains to some extent depending on its density.

as Elevations in S0 Terrengmodell based on mean sea level or the geoid, and since WGS84 ellepsoidal heights were based on the ellepsoid surface, you have to know the geoid height (i.e the difference between the ellepsoid surface and the surface of the geoid - mean sea level surface) to convert the ellepsoidal heights to mean sea level heights which are comparable with the S0 DTM.

For accurate conversion ask for the Norwegian geoid model for the area under consideration, otherwise you can get the estimated geoid from the EGM2008 global geoid model.

1. Suggestion: Why you are using external receiver/s to improve the positioning, I mean you can use built-in system to improve the solution. contact me for further help.

2. In your case, use Matlab, goGPS, GoogleEarthPro, GPS Track Editor, SBG Center or any other related live simulations where you can see each and every details you required.

I believe this is when this particular ephemeris dataset is either first broadcast or first received by a ground receiver.

For GPS this is roughly 2-hours before the Toe

For GLONASS this is roughly 15-minutes before Toe

For Galileo, it's a bit puzzling that the transmission time is like 11-minutes or so after Toe. Does anyone know why?

Euroconsult's report

First, is the receiver authorized for WAAS? You have to purchase that option and have it enabled on the receiver with the auth code. Assuming that you have a WAAS enabled receiver use the following commands for WAAS:

psrdiffsource sbas

sbascontrol enable waas

Hi,

Yes it's exctly that ! The numbre inside the bracket is in reference of the GPS spreading code frequency, 1.032MHz.

of course*

Interpreting your output file in matrix form, here is the description for the columns:

You're welcome Mourtadha Sarhan Sachit ,

I'm pretty sure DD will achieve better results than PPP.

Generally each country has its densified network of continuous GNSS tracking stations with adjusted coordinates (or known coordinates), i.e., an official geodetic reference system.

IGS network stations are really scarce, when you want to work with a short baseline and reduce tracking time.

One suggestion is to track a vertex (Base) for a longer time (for example, 4 hours) and simultaneously track other vertices for 15 min (Semi kinematic Method - Stop and Go), with baselines smaller than 20 km. With this method you can achieve accuracy better of a few centimeters.

If the Base has known coordinates, then you can apply directly the DD method, but if it is not known (which happens in most cases), you should perform static relative processing with the closest stations of the geodetic network in your region.

It's a little complicated to explain everything with a few words, but I hope you have understood the main idea.

I suggest consulting the following book:

Satellite Geodesy, 2nd Ed. Günter Seeber

It covers very well all these methods of positioning.

Dear Okiemute,

I agree with Dr. Ruby. Just want to add, go for 7-parameter model to account for bias and tilts in the geometric and gravimetric values. It presents better results as compared to 4-paramter model.

Regards,

Ropesh Goyal

Dear Mohamed Freeshah,

you could try my Python script (https://github.com/Albom/GNSS_utils/blob/master/test_ionex.py). It produces output data in gnuplot data format. It also possible to plot world map on the same plot.

Dear Yury,

Check out this paper...

"Identification of vertical total electron content by time series analysis, Digital Signal Processing 19(4):740-749, 2009 by Erdogan H and Arslan N."

alright. All clear. Thanks for sharing.

Best Wishes, Jean-Philippe

Montaha,

Adding to the colleagues above, i would like to comment your question from the fundamental point of view. You have three antennas which lay near to each other,

One transmit antennas and two receiving antennas. Every antenna has its own port to connect it to its rf front end. Assume this ports are P1,P2,and P3, respectively.

If the antennas are couples you can measure the coupling between them by the transmission coefficients S21,and S31 with anrtenna 1 is the transmitting antenna. In order to measure how far the antennas are isolated from each other you have to measure S21 and S31 at the intended frequency.

As isolation between antennas increase S21 and S31 will get smaller. For complete isolation S21 and S31 will be zero and the attenuation will be infinity.

So, things are put in its context.

Best wishes

GPS/GNSS receivers do not give orthometric heights, they only give ellipsoidal heights. This is because, the heights are geometric as they are computed with respect to the ellipsoid. Orthometric heights are obtained with respect to the geoid/MSL. Orthometric heights can only be obtained from processed GPS/GNSS observations if the local geoid model of the area of observation is applied.

For nonlinear estimation, both the EKF and the UKF are approximate strategies. The propagation of the error covariance matrix P is simplified by the use of Jacobian in the EKF and the choice of sigma points in the UKF.

Though Q and R matrices generally refer to the process noise covariance and measurement noise covariance respectively, they can also compensate for the errors in the approximation of the propagation of the error covariance P. That is, in the EKF Q can be tuned such that it compensates for the loss of higher order information that result from linearization. The same holds for the UKF too.

To sum up, Q and R can be tuned to represent a little more than process noise and measurement noise for nonlinear Kalman-based estimation schemes. This gives rise to difficulty in tuning for some applications. Since you work with simulated data, you can easily verify if your tuning is reasonably correct by checking if the actual error covariance is approximately close to the one obtained by the filter. This implies that the estimation scheme works reasonably well. In the absence of the true state value as happens in real-world situations, the innovations can be compared.

Coming to the specific case, if you want to do the fair comparison tune both the estimation schemes separately to obtain the best results. Then compare them as pointed out early.

you can compute the SD as

SD = PR - Range

Where PR is measure psuedorange and range can be calculated through distance equation, using own and satellite position.

In ground based systems, survey techniques are utilized to find the own position, which is a preferred method. However in dynamic situations, the measured position can be used as own position. Using measured position in SD is not suitable for finding the error corrections, but could use for integrity calculations.

Dear Charles,

Thank you very much for your message and for the valuable documents. I am also very actively involved in this line of research. A recent paper on GNSS performance threats and augmentation strategies in aviation was recently published in JPAS (for convenience, the link is provided below):

In addition to addressing GNSS issues, it would be nice working together and investigating the feasibility of specific safety features that could be included in the ADS-B data-link as well.

Direct sampling of RF signal requires a notch filter at front-end to reject signal in other bands, otherwise all unwanted signals will also get sampled.

2GS/s is more than enough for working on L5.

The advantage of Direct sampling is that you do not need down conversion, but the requirement of notch filter is too stringent that it is impossible to be realized.

IRNSS RINEX files provides you raw measurements (observation file), satellite ephemeris (nav files). They do not have slant TEC values, you will need to compute them from these measurements.

Just a quick update: The Bern server has been changed to a new host name. The CODE maps can now be retrieved from here: ftp://ftp.aiub.unibe.ch/CODE/

thank you so much sir