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Drone Detection with Chirp-Pulse Radar Based on Target Fluctuation Models
Abstract
This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non‐conducting materials, their radar cross‐section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal‐to‐noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.
... Вищезгаданi переваги дозволяють використовувати FMCW радар у широкому колi застосункiв. Наприклад такi радари використовуються для монiторингу навколишнього простору на наявнiсть безпiлотних лiтальних апаратiв [4][5][6]. Висока роздiльна здатнiсть FMCW радара дозволяє виявляти та розрiзняти близько розташованi об'єкти. А здатнiсть одночасно вимiрювати як дальнiсть, так i радiальну швидкiсть забезпечує можливiсть iдентифiкувати та класифiкувати цiлi за характеристиками їх руху [1]. ...
... На другому етапi знайдемо оцiнкиˆ,ˆ. При цьому оцiнки амплiтуди i початкової фазиˆзнаходяться за формулами (6). ...
Одним з перспективних засобів виявлення рухомих цілей на малих відстанях є FMCW радар. Він забезпечує: високоточне вимірювання дальності, а також радіальної швидкості, низьке енергоспоживання. Вихідною інформацією для алгоритмів виявлення сигналів цілей в FMCW радарі є далекомірно-доплерівський портрет. Він формується шляхом двомірного дискретного перетворення Фур’є (ДПФ) над отриманими на інтервалі когерентного накопичення демодульованими сигналами відповідних періодів модуляції. У випадку однорідного шуму з невідомою потужністю викорстання алгоритмів CFAR призводить до надлишкових обчислювальних витрат за рахунок ковзного оцінювання потужності шуму. Крім того розміри ковзного вікна обмежені, що не дозволяє отримати оцінку потужності шуму з необхідною точністю. В якості математичної моделі корисного сигналу від цілі може бути використаний гармонійний сигнал з невідомими амплітудою, частотою і початковою фазою. Розглянуто алгоритм адаптивного виявлення гармонійного сигналу з невідомими параметрами, отриманого на інтервалі когерентного накопичення FMCW радара при відомій потужності шуму. Пристрій виявлення побудовано за періодограмною схемою. Проведено аналіз характеристик виявлення сигналу FMCW радара при відомій потужності шуму, які можуть виступати в якості нижньої границі при невідомій дисперсії шуму. На основі методу максимальної правдоподібності запропоновано алгоритм оцінювання невідомої потужності шуму на основі тестової вибірки, отриманої з далекомірно-доплерівського портрету. Оцінка невідомої потужності шуму є вибірковим середнім. На основі методу інтервального оцінювання визначено довірчі інтервали щодо ймовірностей хибної тривоги і виявлення цілі в залежності від об’єму тестової вибірки. Границі ймовірності хибної тривоги не залежать від оцінки потужності шуму. На основі отриманих залежностей можна визначити об’єм вибірки, що забезпечує допустиме значення довжини довірчого інтервалу ймовірностей хибної тривоги і виявлення цілі.
... Radar-Based Detection [48][49][50][51]: Radars are commonly utilized for aircraft detection in military and civil fields, including aviation, and are thus recognized as reliable tools for detecting drones. It transmits radio waves, typically in the microwave range, and analyzes the reflected waves to determine the presence, distance, and speed of objects like aircraft or drones [52]. ...
Drones, with their ability to vertically take off and land with their stable hovering performance, are becoming favorable in both civilian and military domains. However, this introduces risks of its misuse, which may include security threats to airports, institutes of national importance, VIP security, drug trafficking, privacy breaches, etc. To address these issues, automated drone detection systems are essential for preventing unauthorized drone activities. Real-time detection requires high-performance devices such as GPUs. For our experiments, we utilized the NVIDIA Jetson Nano to support YOLOv9-based drone detection. The performance evaluation of YOLOv9 to detect drones is based on metrics like mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score. Experimental data revealed significant improvements over previous models, with a mAP of 95.7%, a precision of 0.946, a recall of 0.864, and an F1-score of 0.903, marking a 4.6% enhancement over YOLOv8. This paper utilizes YOLOv9, optimized with pre-trained weights and transfer learning, achieving significant accuracy in real-time drone detection. Integrated with the NVIDIA Jetson Nano, the system effectively identifies drones at altitudes ranging from 15 feet to 110 feet while adapting to various environmental conditions. The model’s precision and adaptability make it particularly suitable for deployment in security-sensitive areas, where quick and accurate detection is crucial. This research establishes a solid foundation for future counter-drone applications and shows great promise for enhancing situational awareness in critical, high-risk environments.
... To safeguard against the potential risks presented by unmanned aircraft, it's essential to employ a range of surveillance technologies across border areas, enabling the timely detection of any objects that could pose a threat. These measures are vital in maintaining the integrity of national boundaries and ensuring a proactive defense against any unidentified aerial incursions (for example, [1][2][3]). One of the important issues here is to be able to direct the monitoring camera towards this object as soon as possible in order to be able to track the object when a suspicious flying object is detected by the monitoring camera in the border zone [4]. ...
In this research, a mathematical model was proposed to calculate the shortest time for the video camera to be directed to the object when a suspicious object is detected in the border zone monitored by video cameras.
... UAV has the advantages of high maneuverability, small size, and sensitive operation. It is widely used in transportation, military, scientific research, and other fields [11][12][13]. The development of computer technology and image recognition technology has made moving target detection technology more and more mature, and the use of unmanned aerial vehicles can make moving target detection technology to a higher level. ...
The detection of moving objects by machine vision is a hot research direction in recent years. It is widely used in military, medical, transportation, and agriculture. With the rapid development of UAV technology, as well as the high mobility of UAVs and the wide range of high-altitude vision, the target detection technology based on UAV vision is applied to traffic management such as vehicle tracking and detection of vehicle violations. The moving target detection technology in this study is based on the YOLOv3 algorithm. It implements moving vehicle tracking by means of Mean-Shift and Kalman filtering. In this paper, the Gaussian background difference technology is used to analyze the illegal behavior of the vehicle, and the color feature extraction technology is used to identify and locate the license plate, and the information of the illegal vehicle is entered into the database. The experiment compares the moving target detection of UAV vision and the traditional target detection in four aspects: recognition accuracy, recognition speed, manual time, and divergent results. The results show that the average accuracy rates of UAV vision-based moving target detection and traditional pattern recognition are 98.4% and 87.8%, respectively. The recognition speeds are 24.9 (vehicles/sec) and 10.6 (vehicles/sec), respectively. However, the artificial time and divergence results of moving target detection based on UAV vision are only 1/3 of the traditional mode. The moving target detection based on UAV vision has a better moving target detection ability.
... Specific systems suggested by researchers present autonomous detection, monitoring and identification. The proposed systems make use of technologies such as radio frequency signals (Nguyen, Ravindranatha, Nguyen, Han & Vu, 2016), LIDAR (Hammer, Hebel, Laurenzis & Arens, 2018), RADAR (Kim, Park, Park, Kim, Jung, Kim et al., 2018;Shin, Jung, Kim, Ham & Park, 2016), audio assistance (Anwar, Kaleem & Jamalipour, 2019;Kim, Park, Ahn, Ko, Park & Gallagher, 2017;, and vision-based systems (Demir, Ergunay, Nurlu, Popovic, Ott, Wellig et al., 2020;Unlu, Zenou, Riviere & Dupouy, 2019). A general overview of UAV detection technologies and their pros and cons can be found on studies by Azari, Sallouha, Chiumento, Rajendran, Vinogradov and Pollin (2018) and Guvenc, Koohifar, Singh, Sichitiu and Matolak (2018). ...
Purpose: The aim of this study is twofold. First is to compare manned and unmanned aviation regulations in the context of ICAO Annexes to identify potential deficiencies in the international UAV legislations. Second is to propose a UAV monitoring module work flow as a solution to identified deficiencies in the international UAV regulations.
Design/methodology/approach: In the present study, firstly the regulations used in manned aviation were summarized in the context of ICAO Annexes. Then along with an overview of the use of UAVs, international UAV regulations have been reviewed with a general perspective. In addition, a comparison was made on whether contents of ICAO Annexes find a place in common international UAV regulations in order to understand areas to be developed in the international UAV regulations, and to better understand the different principles between manned and unmanned air transport. In the last section, we present a UAV tracking module (UAVTram) in line with the above-mentioned comparison between manned and unmanned aviation and the identified deficiencies in the international UAV regulations.
Findings: The international UAV regulations should be developed on the basis of airport airspace use, detection, liabilities, sanctions of violations, and updating of regulation. Proposed UAVTram has potential to offer real-time tracking and detection of UAVs as a solution to malicious use of UAVs.
Research limitations/implications: Our study is not exempt from limitations. Firstly, we didn’t review all UAV regulations because it needs a considerable amount of efforts to check out all the UAV regulations pertinent to different areas of the world. It is the same case for manned aviation as we used only ICAO Annexes to contextually compare with UAV regulations.
Practical implications: From the practical perspective, studies introducing new technologies such as systems that help detection of remote pilots causing trouble and agile defense systems will give valuable insights to remove individual UAV threats.
Originality/value: We didn’t find any study aiming to compare manned and unmanned aviation rules in search of finding potential deficiencies in the UAV regulations. Our study adopts such an approach. Moreover, our solution proposal here uses Bluetooth 5.0 technology mounted on stationary transmitters which provides more effective range with higher data transfer. Another advantage is that this work is projected to be supported by Turkish civil aviation authority, DGCA. This may accelerate efforts to make required real-time tests.
... A prototype UAV detection system was developed by KAIST to investigate target fluctuation characteristics [25]. This is a 34.5 GHz chirp-pulse radar using three pulse widths for range ambiguity resolution. ...
... As the optimization process is a task to find a phase matrix S, we selected L, M, and N to constitute the phase matrix S as independent variables, and λ was used for the objective function. The chirp bandwidth and pulse width were fixed to 16 MHz and 1 µs, respectively (the netted radar system for detecting drones normally consists of up to 8 radars and uses the same pulse length of 1 µ to perform the same role [19] and 16 MHz, respectively). The sampling rate used in the MATLAB simulation was set to 256 MHz. ...
To meet the increasing demands for remote sensing, a number of radar systems using Linear Frequency Modulation (LFM) waveforms have been deployed, causing the problem of depleting frequency resources. To address this problem, several researchers have proposed the Spectrum Shared Radar System (SSRS) in which multiple radars share the same frequency band to transmit and receive their own signals. To mitigate the interferences caused by the signal transmission by other radars, SSRS employs orthogonal waveforms that inherit the orthogonality of the waveforms from orthogonal codes. However, the inherited orthogonality of the codes is significantly reduced when incorporating LFM waveforms with the codes. To solve this problem, in this paper, we propose a novel but simple scheme for generating a set of optimized coded LFM waveforms via new optimization framework. In the optimization framework, we minimize the weighted sum of autocorrelation sidelobe peaks (ASP) and cross-correlation peaks (CP) of the coded LFM waveforms to maximize the orthogonality of the waveforms. Through computer simulations, we show that the waveforms generated by the proposed scheme outperform the waveforms created by previous proposals in terms of ASP and CP.
... While several researchers have recently investigated the problem of micro-UAV detection using mmWave radars [6], [7], to the best of our knowledge, none of these works have considered the effects of the non-Rayleigh This work has been supported by the NASA grant NNX17AJ94A. clutter statistics of the terrain. ...
Millimeter wave radars are popularly used in last-mile radar-based defense systems. Detection of low-altitude airborne target using these radars at low-grazing angles is an important problem in the field of electronic warfare, which becomes challenging due to the significant effects of clutters in the terrain. This paper provides both experimental and analytical investigation of micro unmanned aerial vehicle (UAV) detection in a rocky terrain using a low grazing angle, surface-sited 24 GHz dual polarized frequency modulated continuous wave (FMCW) radar. The radar backscatter signal from the UAV is polluted by land clutters which is modeled using a uniform Weibull distribution. A constant false alarm rate (CFAR) detector which employs adaptive thresholding is designed to detect the UAV in the rich clutter background. In order to further enhance the discrimination of the UAV from the clutter, the micro-Doppler signature of the rotating propellers and bulk trajectory of the UAV are extracted and plotted in the time-frequency domain.
... While several researchers have recently investigated the problem of micro-UAV detection using mmWave radars [6], [7], to the best of our knowledge, none of these works have considered the effects of the non-Rayleigh This work has been supported by the NASA grant NNX17AJ94A. clutter statistics of the terrain. ...
... While several researchers have recently investigated the problem of micro-UAV detection using mmWave radars [6], [7], to the best of our knowledge, none of these works have considered the effects of the non-Rayleigh This work has been supported by the NASA grant NNX17AJ94A. clutter statistics of the terrain. ...
Millimeter wave radars are popularly used in last-mile radar-based defense systems. Detection of low-altitude airborne target using these radars at low-grazing angles is an important problem in the field of electronic warfare, which becomes challenging due to the significant effects of clutters in the terrain. This paper provides both experimental and analytical investigation of micro unmanned aerial vehicle (UAV) detection in a rocky terrain using a low grazing angle, surface-sited 24 GHz dual polarized frequency modulated continuous wave (FMCW) radar. The radar backscatter signal from the UAV is polluted by land clutters which is modeled using a uniform Weibull distribution. A constant false alarm rate (CFAR) detector which employs adaptive thresholding is designed to detect the UAV in the rich clutter background. In order to further enhance the discrimination of the UAV from the clutter, the micro-Doppler signature of the rotating propellers and bulk trajectory of the UAV are extracted and plotted in the time-frequency domain.
... Low visibility due to small dimensions. Reduced RADAR signature [2][3][4][5][6]. Reduced acoustic signature. ...
With the advent of micro-sized unmanned helicopters and airplanes weighing under 250 g certain needs for measurement instruments and setups are emerging. The authors have identified the lack of micro-motor measurement instruments and more specifically a static thrust gauge device. For this reason, scales to measure static motor thrust was advised and further developed as a laboratory setup.
The motors that are subject to testing with the described instrument are the brushed micro-sized coreless electric motors, well suited for micro-drones with total weight under 250 g. The scale is fitting different sizes of micro-motors and appropriate control of the input voltage is provided. The instrument is measuring simultaneously the applied voltage to the motor and the current it consumes, along with the static thrust the motor generates. The scale is versatile – it measures both pusher and tractor propeller configurations. Pusher propellers in micro-drones are gaining significant attention lately due to their better efficiency characteristics.
The generation of multi-format linearly chirped microwave waveforms (LCMW) based on dual-domain mode-locked optoelectronic oscillator (DDML OEO) is numerically and experimentally demonstrated. In the DDML OEO, the frequency domain mode locking (FDML) can be implemented using a frequency-scanning microwave photonic filter (MPF), which is realized based on a chirped laser diode (LD). The time domain mode locking (TDML) is realized by the period loop loss modulation via the intensity modulation of a dual-drive Mach–Zehnder modulator (DDMZM). The phase domain mode locking can be achieved using the phase modulation in the DDMZM. Controlling the OEO working at a time-frequency domain mode-locking (TFDML) state, pulsed LCMW or multi-band pulsed LCMW signals are generated when fundamental or ultra-high-order harmonic TDML is implemented. Controlling the OEO working as a phase-frequency mode locking (PFDML) state, a phase-coded LCMW signal is generated. In the experiment, a pulsed LCMW signal with a bandwidth of 8 GHz, a pulse width of 7.5 μs, and a repetition period of 12.3 μs is generated. A five-band pulsed LCMW signal is generated, and each band has a bandwidth of 1.3 GHz and a pulse repetition period of 0.41 ns. A phase-coded LCMW signal with a phase coding rate of about 50 Mb/s, a bandwidth of 2.3 GHz, and a repetition period of 12.3 μs is generated.
We propose a 10‐GHz 2 × 2 phased‐array radio frequency (RF) receiver with an 8‐bit linear phase and 15‐dB gain control range using 65‐nm complementary metal–oxide–semiconductor technology. An 8 × 8 phased‐array receiver module is implemented using 16 2 × 2 RF phased‐array integrated circuits. The receiver chip has four single‐to‐differential low‐noise amplifier and gain‐controlled phase‐shifter (GCPS) channels, four channel combiners, and a 50‐Ω driver. Using a novel complementary bias technique in a phase‐shifting core circuit and an equivalent resistance‐controlled resistor–inductor–capacitor load, the GCPS based on vector–sum structure increases the phase resolution with weighting‐factor controllability, enabling the vector–sum phase‐shifting circuit to require a low current and small area due to its small 1.2‐V supply. The 2 × 2 phased‐array RF receiver chip has a power gain of 21 dB per channel and a 5.7‐dB maximum single‐channel noise‐figure gain. The chip shows 8‐bit phase states with a 2.39° root mean‐square (RMS) phase error and a 0.4‐dB RMS gain error with a 15‐dB gain control range for a 2.5° RMS phase error over the 10 to10.5‐GHz band.
1 НАЦІОНАЛЬНИЙ УНІВЕРСИТЕТ ОБОРОНИ АЗЕРБАЙДЖАНСЬКОЇ РЕСПУБЛІКИ НАЦІОНАЛЬНИЙ ТЕХНІЧНИЙ УНІВЕРСИТЕТ "ХАРКІВСЬКИЙ ПОЛІТЕХНІЧНИЙ ІНСТИТУТ" ХАРКІВСЬКИЙ НАЦІОНАЛЬНИЙ УНІВЕРСИТЕТ РАДІОЕЛЕКТРОНІКИ НАЦІОНАЛЬНИЙ АЕРОКОСМІІЧНИЙ УНІВЕРСИТЕТ ІМЕНІ М. Є. ЖУКОВСЬКОГО "ХАРКІВСЬКИЙ АВІАЦІЙНИЙ ІНСТИТУТ" УНІВЕРСИТЕТ МІСТА ЖИЛІНА СУЧАСНІ НАПРЯМИ РОЗВИТКУ ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНИХ ТЕХНОЛОГІЙ ТА ЗАСОБІВ УПРАВЛІННЯ Тези доповідей чотирнадцятої міжнародної науково-технічної конференції 25-26 квітня 2024 року Том 1: секції 1, 2 Баку-Харків-Жиліна-2024
1 НАЦІОНАЛЬНИЙ УНІВЕРСИТЕТ ОБОРОНИ АЗЕРБАЙДЖАНСЬКОЇ РЕСПУБЛІКИ НАЦІОНАЛЬНИЙ ТЕХНІЧНИЙ УНІВЕРСИТЕТ "ХАРКІВСЬКИЙ ПОЛІТЕХНІЧНИЙ ІНСТИТУТ" ХАРКІВСЬКИЙ НАЦІОНАЛЬНИЙ УНІВЕРСИТЕТ РАДІОЕЛЕКТРОНІКИ НАЦІОНАЛЬНИЙ АЕРОКОСМІІЧНИЙ УНІВЕРСИТЕТ ІМЕНІ М. Є. ЖУКОВСЬКОГО "ХАРКІВСЬКИЙ АВІАЦІЙНИЙ ІНСТИТУТ" УНІВЕРСИТЕТ МІСТА ЖИЛІНА СУЧАСНІ НАПРЯМИ РОЗВИТКУ ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНИХ ТЕХНОЛОГІЙ ТА ЗАСОБІВ УПРАВЛІННЯ Тези доповідей чотирнадцятої міжнародної науково-технічної конференції 25-26 квітня 2024 року Том 1: секції 1, 2 Баку-Харків-Жиліна-2024
The review emphasizes ways for identifying and removing existing drone technology that the air defense system (ADS) should implement in order to avoid future drone strikes. Because radar systems are no longer effective for contemporary drone identification, alternative sensors based on sound (microphones), light (cameras), and radio frequency (RF) can be used. Methodologies such as RF jammers, GPS spoofers, and high-power microwave (HPM), and high-energy lasers should also be utilized for drone eradication. These methods are ideally suited for non-stealthy unmanned aerial vehicles (UAVs) and small drones. For stealth, other methods such as quantum radar and quantum LiDAR and space-based infrared detectors can be utilized. The paper investigates signatures of micro-Doppler effect due to the back-and-forth motion of two blades single-propeller drones. Furthermore, numerical study of other stealth aircraft detection methods such as quantum radar and quantum LiDAR and space-based infrared detectors is reviewed in-depth.
In the Hayabusa2 re-entry capsule recovery operation, marine radars were used to estimate the landing point of the capsule. These radars were commercial products for ships and were used with some modifications for this operation. The radars tracked the instrument module (I/M) containing the asteroid samples for 18 min, along with other equipment. The maximum observed distance was 31.55 km, and the tracking was accurate up to 240 m near the I/M landing point. This article describes the marine radar system, reports the tracking results of the operation, and discusses the considerations made for future missions. One consideration is to estimate the altitude by using multiple search radars in order to more accurately predict the landing point. Another is to estimate the radar cross-section of the I/M during descent. This article demonstrates that the altitude can be estimated by the search radars, and the radar cross-section fluctuates by approximately. 10 times depending on the elevation angle relative to the radar. The results of this article suggest the possibility of cooperative search by using multiple small radars. This feature may contribute to reductions in mission costs and to expanding the choice of landing areas.
Radar surveillance of unmanned aerial vehicles (UAVs) is actively developing area of scientific research. This article provides a review and analysis of publications in recent years devoted to the methods and radar systems of detection and recognition of classes and types of UAVs. It is noted that the most difficult targets for radar detection are low-sized, low-speed small UAVs (drones) flying at low and extremely low altitudes. If large and medium-sized UAVs can be detected by modern radar systems, then for the detection of small UAVs it is advisable to create specialized highly efficient, highly mobile, portable and inexpensive active UAV detection radars. The technical requirements for such radars are defined and recommendations for their implementation are provided. High-performance protection systems based on adaptive lattice filters are offered to protect UAV detection radars from noise jamming and passive interference. It is shown that the research on the methods of recognizing UAV classes and types is a development of the existing theory and technology of radar recognition of air targets.
We propose 8.2‐GHz band radar RFICs for an 8 × 8 phased‐array frequency‐modulated continuous‐wave receiver developed using 65‐nm CMOS technology. This receiver panel is constructed using a multichip solution comprising fabricated 2 × 2 low‐noise amplifier phase‐shifter (LNA‐PS) chips and a 4ch RX front‐end chip. The LNA‐PS chip has a novel phase‐shifter circuit for low‐voltage operation, novel active single‐to‐differential/differential‐to‐single circuits, and a current‐mode combiner to utilize a small area. The LNA‐PS chip shows a power gain range of 5 dB to 20 dB per channel with gain control and a single‐channel NF of 6.4 dB at maximum gain. The measured result of the chip shows 6‐bit phase states with a 0.35˚ RMS phase error. The input P1 dB of the chip is approximately –27.5 dBm at high gain and is enough to cover the highest input power from the TX‐to‐RX leakage in the radar system. The gain range of the 4ch RX front‐end chip is 9 dB to 30 dB per channel. The LNA‐PS chip consumes 82 mA, and the 4ch RX front‐end chip consumes 97 mA from a 1.2 V supply voltage. The chip sizes of the 2 × 2 LNA‐PS and the 4ch RX front end are 2.39 mm × 1.3 mm and 2.42 mm × 1.62 mm, respectively.
We use two traditional indices and propose a new fusion index to quantify the echo chirp signal amplification effectively. They are the cross-correlation coefficient, the spectral amplification factor and the fusion index. Through investigating different numerical examples, we verify the validation of the re-scaled vibrational resonance (VR) method for the echo chirp signal by using the two traditional indices. Besides, taking into account that a single index has its own shortness, we combine the advantages of the two traditional indices mentioned and propose a new fusion index based on the information fusion method. Then we verify the superiority of the fusion index. Furthermore, the effect of noise on VR is also investigated. Finally, with the help of the adaptive inertia weight particle swarm optimization (APSO) algorithm, we propose the adaptive VR in noise background and verify its validity.
Poster showing key results of UAV and birds detections using a multistatic radar sensor.
This paper presents the use of micro-Doppler signatures collected by a multistatic radar to detect and discriminate between micro-drones hovering and flying while carrying different payloads, which may be an indication of unusual or potentially hostile activities. Different features have been extracted and tested, namely features related to the Radar Cross Section of the micro-drones, as well as the Singular Value Decomposition (SVD) and centroid of the micro-Doppler signatures. In particular, the added benefit of using multistatic information in comparison with conventional radar is quantified. Classification performance when identifying the weight of the payload that the drone was carrying while hovering was found to be consistently above 96% using the centroid-based features and multistatic information. For the non-hovering scenarios classification results with accuracy above 95% were also demonstrated in preliminary tests in discriminating between three different payload weights.
Human activity recognition is an emerging technology for many security, surveillance, and health service applications utilizing wireless sensor networks (WSNs). However, the exploitation of radar in WSNs has been only recently made possible through the development of small, low-power, and low-cost wireless radar motes, such as the BumbleBee radar developed by the Samraksh Company. This letter explores the capacity of using the BumbleBee radar for indoor human activity classification based on micro-Doppler signatures. The electromagnetic measurements of the signal transmitted by the BumbleBee radar are made to fully characterize the sensor and its limitations. A database of the multiperspective micro-Doppler signatures measured from the BumbleBee radar is compiled to analyze the classification performance and limitations due to the dwell time and the aspect angle. Within the operational constraints delineated, it is shown that the BumbleBee radar can be used to discriminate between walking, running, and crawling, even under variable conditions.
This report addresses the development of a methodology for dealing with staggered PRI coherent dwells in radar through the application of multirate signal analysis. The performance of multiple PRF systems is discussed along with the discussion of a number of techniques to obtain a spectrum from nonuniformly sampled data. The methods described include: Interpolating in spatial domain by polynomials; Chinese remainder theorem and the clustering algorithm; Least squares method; Multi-resolution analysis; Iterative method; Orthogonal Polynomial Expansions; and Estimation from the Analog Frequency. The report closes out with a comparison of various methods and a Conclusions section.
In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter.
Micro-Doppler effects in narrowband radar are introduced in this chapter. We mainly analyze the micro-Doppler effects induced by rotations, vibrations, and precessions of targets. Rotations, vibrations, and precessions are typical micromotion dynamics of targets in the real world. Typical rotations include rotations of helicopter rotors, mechanical scanning radar antennas, turbine blades, etc. Typical vibrations include engine-induced car surface vibrations, mechanical oscillations of a bridge, etc. Precession is a major movement form of spatial targets, for example, precessions usually go with a ballistic missile in its midcourse flight. Analyzing the micro-Doppler effects of rotations, vibrations, and precessions provides a basic foundation for further discussions on micro-Doppler effects of complicated motions. The influence of radar platform vibration on micro-Doppler effect analysis is also investigated. Finally, related MATLAB codes are provided on the companion Website: http://booksite.elsevier.com/9780128098615/ .
Herein, a novel method of radar target detection based on 2-dimensional (2-D) fractal dimension is proposed. The proposed approach exploits both range information and azimuth information to estimate fractal dimension. Moreover, the approach can increase the number of data points. The above two merits result in the fractal dimension estimated by this method are more accurate and robust than the previous method. The detection performance is also better than the previous, which only makes use of 1-dimensional (1-D) information to estimate fractal dimension. Theoretical analysis and experimental results show that the proposed method performs well in a strong clutter background. The proposed method is also validated by real-life radar data, and the better result has been achieved.
We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in the conventional supervised learning paradigm that relies on the design of handcrafted features. Whereas these schemes attained high accuracy, the requirement for domain knowledge of each problem limits the scalability of the proposed schemes. In this letter, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms, directly to a raw micro-Doppler spectrogram for both human detection and activity classification problem. The DCNN can jointly learn the necessary features and classification boundaries using the measured data without employing any explicit features on the micro-Doppler signals. We show that the DCNN can achieve accuracy results of 97.6% for human detection and 90.9% for human activity classification.
The multistatic radar proposed in this paper is composed of a transmitter, placed in a high altitude site, at least four receivers distributed in a low altitude area and a central processing station. The transmitter emits a FMCW signal in the air which is reflected by the natural environment and the targets inside the radar's coverage area. The line of sight signal and the collected signal scattered from the surrounding objects are collected from each receiver and a processing algorithm is applied to measure range/doppler and separate the moving air vehicles from the stationary targets (clutter). The targets' range/doppler measurements from every receiver are sent to a central processing station where data fusion of the distributed measurements is performed. A list of the existing tracks is maintained, updated and managed, while deghosting is also applied to remove the pseudo-targets generated from geometrical ambiguities. The main goal of this multistatic radar system is to take advantage of the bistatic RCS to increase the probability of detection of targets with very low monostatic RCS. In addition, the transmitter's task is limited only to produce the desired waveform while the receiver performs a totally passive process, which makes it less vulnerable to intercept. Finally, it must be noticed that proper techniques are used for the estimation of the targets' characteristics while the receiver's operation remains simple and low cost.
The theoretical return signal from aircraft propeller blades is
analyzed. The basic theory involved is described, some simulation
results are examined, and some practical considerations are discussed.
It is shown that the modulation contained in the return signal is a form
of frequency modulation and results in a number of sidebands about the
center frequency of the target. It has also been shown that the
modulation is due to six main variables, four of which are parameters of
the propeller blades, one of which depends on the radar, and one of
which depends on the aspect angle of the propeller
This report considers the probability of detection off a target by a pulsed search radar, when the target has a fluctuating cross section. Formulas for detection probability are derived, and curves off detection probability vs, range are given, for four different target fluctuation models. The investigation shows that, for these fluctuation models, the probability of detection for a fluctuating target is less than that for a non-fluctuating target if the range is sufficiently short, and is greater if the range is sufficiently long. The amount by which the fluctuating and non-fluctuating cases differ depends on the rapidity of fluctuation and on the statistical distribution of the fluctuations. Figure 18, p. 307, shows a comparison between the non-fluctuating case and the four fluctuating cases considered.
This paper gives a theoretical solution to the problem of
determining the electromagnetic backscattering and Doppler spectrum of
an aircraft propeller as presented to a radar operating in the 8-12 GHz
band. At this band for all practical aircraft propeller the
electromagnetic backscattering regime is in the optical region. The
solution proceeds by modeling the aircraft propeller as a set of
multiple skew-plated metal fan blades in the presence of a linearly
polarized EM wave. Based on the quasi-stationary method combined with
physical optics and physical theory of diffraction equivalent currents
techniques are used to analyse the backscattering from aircraft
propeller blades. Experimental results indicated that, in the far zone,
the field can be considered as harmonic and expressions for the spectral
components of the field are obtained. The observed waveforms are found
to be in good agreement with theoretical results
- Sarkar
Analysis of the Effects of Blade Pitch on the Radar Return Signal from Rotating Aircraft Blades
- J Martin
- B Mulgrew
J. Martin and B. Mulgrew, "Analysis of the Effects of
Blade Pitch on the Radar Return Signal from Rotating
Aircraft Blades," Int. Conf. Radar, Oct. 1992, pp. 446-449.
35 GHz FMCW Drone Detection System
- J Drozdowicz
J. Drozdowicz et al., "35 GHz FMCW Drone Detection
System," Int. Radar Symp., Krakow, Poland, May 10-12,
2016, pp. 1-4.
Detection of UAVs Using the MIMO Radar Miracle-KA
- J Klare
- O Biallawons
- D Cerutti-Maori
J. Klare, O. Biallawons, and D. Cerutti-Maori, "Detection
of UAVs Using the MIMO Radar Miracle-KA," Proc.
EUSAR 2016: Eur. Conf. Synthetic Aperture Radar,
Hamburg, Germany, June 6-9, 2016, pp. 1-4.