The resolution of radar images is constantly increasing. As a result, radar images require more storage space, which is associated with increased costs. Therefore, it is advantageous to minimize the data size. In this paper, we present various compression methods for reducing the data size of radar images. Compression and decompression are performed in two scenarios. In the first scenario, the raw data are compressed and decompressed before the image is reconstructed. In the second scenario, the reconstructed image itself is compressed and decompressed. In both scenarios, the reconstructed radar image is compared with the original image. Due to its widespread use, High-Efficiency Video Coding (HEVC) is used as a state-of-the-art benchmark for both scenarios and compared with proprietary algorithms that combine lossy and lossless compression. A discrete Fourier transform–based compression algorithm from the automotive sector is used as another state-of-the-art benchmark. This is applied against our novel approaches, which are based on the discrete cosine transform, use of direct thresholding in the spatial domain, or are applied to the maximum intensity projection. With the exception of HEVC, all algorithms presented have in common that they perform lossy data processing in the first step and then use the Lempel–Ziv–Markov algorithm as a lossless compression step. To compare the compression ratios, we use various image- and video-specific metrics, such as the peak signal–to-noise ratio (PSNR), the similarity of speeded-up robust features, and the structural similarity index measure (SSIM). For a simple classification, we use Otsu’s method to examine the effects of compression on the images. The radar images are categorized into transparent and nontransparent based on the measurement objects. Depending on the application and the desired resolution, our approaches can achieve storage savings of up to 99.93 % compared to the uncompressed data with PSNR and SSIM values of 38.8 dB and 0.916, respectively.
{"title":"Data Compression for Close-Range Radar Imaging","authors":"Rainer Rückert;Ingrid Ullmann;Christian Herglotz;André Kaup;Martin Vossiek","doi":"10.1109/TRS.2024.3387288","DOIUrl":"https://doi.org/10.1109/TRS.2024.3387288","url":null,"abstract":"The resolution of radar images is constantly increasing. As a result, radar images require more storage space, which is associated with increased costs. Therefore, it is advantageous to minimize the data size. In this paper, we present various compression methods for reducing the data size of radar images. Compression and decompression are performed in two scenarios. In the first scenario, the raw data are compressed and decompressed before the image is reconstructed. In the second scenario, the reconstructed image itself is compressed and decompressed. In both scenarios, the reconstructed radar image is compared with the original image. Due to its widespread use, High-Efficiency Video Coding (HEVC) is used as a state-of-the-art benchmark for both scenarios and compared with proprietary algorithms that combine lossy and lossless compression. A discrete Fourier transform–based compression algorithm from the automotive sector is used as another state-of-the-art benchmark. This is applied against our novel approaches, which are based on the discrete cosine transform, use of direct thresholding in the spatial domain, or are applied to the maximum intensity projection. With the exception of HEVC, all algorithms presented have in common that they perform lossy data processing in the first step and then use the Lempel–Ziv–Markov algorithm as a lossless compression step. To compare the compression ratios, we use various image- and video-specific metrics, such as the peak signal–to-noise ratio (PSNR), the similarity of speeded-up robust features, and the structural similarity index measure (SSIM). For a simple classification, we use Otsu’s method to examine the effects of compression on the images. The radar images are categorized into transparent and nontransparent based on the measurement objects. Depending on the application and the desired resolution, our approaches can achieve storage savings of up to 99.93 % compared to the uncompressed data with PSNR and SSIM values of 38.8 dB and 0.916, respectively.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"421-433"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10496282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.1109/TRS.2024.3385181
Junli Chen;Mingliang Tao;Yifei Liu;Tao Li;Yanyang Liu;Jieshuang Li;Chuheng Tang;Jiawang Li;Ling Wang
The LuTan-1 satellite is the first Chinese, L-band, distributed, spaceborne interferometric synthetic aperture radar (InSAR) mission. However, the presence of radio frequency interference (RFI) in the L-band poses a significant threat to obtaining a high-quality digital elevation model (DEM) and deformation monitoring. This paper provides a first investigation and assessment of the RFI issues in the operational LuTan-1 InSAR system. The RFI environments are analyzed from the status of frequency allocation. The mathematical model of interference in InSAR image pairs is derived and discussed the variation of interferometry coherence under different imaging modes. Furthermore, this paper proposes an automatic processing pipeline of RFI detection and mitigation for the LuTan-1 ground processing system, which is efficient for dealing with massive images without tuning hyperparameters. Extensive experimental results on diverse scenes in LuTan-1 real measured data with different RFI cases are provided, including the single-pass, repeat-pass, and full polarization modes. Experimental results verify that the proposed detection and mitigation scheme could effectively eliminate the RFI artifacts, enhance the image quality, and improve the interferometric coherence. The proposed RFI detection and mitigation scheme has been successfully incorporated into the LuTan-1 ground processing pipeline.
{"title":"Characterization and Mitigation of Radio Frequency Interference Signatures in L-Band LuTan-1 InSAR System: First Results and Assessment","authors":"Junli Chen;Mingliang Tao;Yifei Liu;Tao Li;Yanyang Liu;Jieshuang Li;Chuheng Tang;Jiawang Li;Ling Wang","doi":"10.1109/TRS.2024.3385181","DOIUrl":"https://doi.org/10.1109/TRS.2024.3385181","url":null,"abstract":"The LuTan-1 satellite is the first Chinese, L-band, distributed, spaceborne interferometric synthetic aperture radar (InSAR) mission. However, the presence of radio frequency interference (RFI) in the L-band poses a significant threat to obtaining a high-quality digital elevation model (DEM) and deformation monitoring. This paper provides a first investigation and assessment of the RFI issues in the operational LuTan-1 InSAR system. The RFI environments are analyzed from the status of frequency allocation. The mathematical model of interference in InSAR image pairs is derived and discussed the variation of interferometry coherence under different imaging modes. Furthermore, this paper proposes an automatic processing pipeline of RFI detection and mitigation for the LuTan-1 ground processing system, which is efficient for dealing with massive images without tuning hyperparameters. Extensive experimental results on diverse scenes in LuTan-1 real measured data with different RFI cases are provided, including the single-pass, repeat-pass, and full polarization modes. Experimental results verify that the proposed detection and mitigation scheme could effectively eliminate the RFI artifacts, enhance the image quality, and improve the interferometric coherence. The proposed RFI detection and mitigation scheme has been successfully incorporated into the LuTan-1 ground processing pipeline.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"404-420"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-29DOI: 10.1109/TRS.2024.3406883
Katsuhisa Kashiwagi;Koichi Ichige
We have developed a versatile dataset generation system for hand gesture (HG) recognition using frequency-modulated continuous-wave (FMCW)-multi-input-multioutput (MIMO) radar to improve the classification performance compared to conventional methods such as open dataset, other data generators using a generative adversarial network (GAN), and motion capture tools. The proposed system consists of an HG trajectory generator, an intermediate frequency (IF) signal generator corresponding to antenna locations, and a sampling timing generator without any open datasets or any motion capture data utilizing other sensors. After the training is performed by the generated dataset, the testing is carried out by actual data collected from FMCW-MIMO radar. Our findings show that the accuracy of 98% can be achieved with the generated dataset, and the proposed system is available for pretraining without using an actual dataset. Furthermore, when the mixed dataset is used for the training process, the accuracy improves by almost 37 points compared to when using the actual dataset only.
{"title":"Versatile Dataset Generation System for Hand Gesture Recognition Utilizing FMCW-MIMO Radar","authors":"Katsuhisa Kashiwagi;Koichi Ichige","doi":"10.1109/TRS.2024.3406883","DOIUrl":"https://doi.org/10.1109/TRS.2024.3406883","url":null,"abstract":"We have developed a versatile dataset generation system for hand gesture (HG) recognition using frequency-modulated continuous-wave (FMCW)-multi-input-multioutput (MIMO) radar to improve the classification performance compared to conventional methods such as open dataset, other data generators using a generative adversarial network (GAN), and motion capture tools. The proposed system consists of an HG trajectory generator, an intermediate frequency (IF) signal generator corresponding to antenna locations, and a sampling timing generator without any open datasets or any motion capture data utilizing other sensors. After the training is performed by the generated dataset, the testing is carried out by actual data collected from FMCW-MIMO radar. Our findings show that the accuracy of 98% can be achieved with the generated dataset, and the proposed system is available for pretraining without using an actual dataset. Furthermore, when the mixed dataset is used for the training process, the accuracy improves by almost 37 points compared to when using the actual dataset only.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"561-572"},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.1109/TRS.2024.3382956
Ghania Fatima;Petre Stoica;Augusto Aubry;Antonio De Maio;Prabhu Babu
In this paper, we propose a numerical method for the optimal placement of the receivers in a multistatic target localization system (with a single transmitter and multiple receivers) in order to improve the achievable target estimation accuracy of time-sum-of-arrival (TSOA) localization techniques, for 2D and 3D scenarios. The proposed algorithm is based on the principle of block majorization minimization (block MM) which is a combination of block coordinate descent and majorization-minimization (MM) methods. More precisely, we formulate the design objective for the placement of sensors performing TSOA measurements using $A-$ and $D-$ optimality criteria, and propose an iterative algorithm to find the optimal solution by first splitting the design variable into M blocks (where M is the number of receivers) and then applying the principle of MM on each block. The proposed method can additionally handle the cases where the transmitter also acts as a receiver. The framework can also be applied to the case of non-uniform noise variances at the receivers. Several numerical simulation results are included to show the benefits offered by the developed design algorithm.
在本文中，我们提出了一种在多静态目标定位系统（具有单个发射器和多个接收器）中优化接收器位置的数值方法，以提高到达时间总和（TSOA）定位技术在二维和三维场景下可实现的目标估计精度。所提出的算法基于块大化最小化（block MM）原理，是块坐标下降和大化最小化（MM）方法的结合。更确切地说，我们使用 $A-$ 和 $D-$ 最优标准制定了执行 TSOA 测量的传感器位置设计目标，并提出了一种迭代算法，通过首先将设计变量分成 M 个块（M 为接收器数量），然后在每个块上应用 MM 原则，找到最优解。所提出的方法还能处理发射器同时充当接收器的情况。该框架还可应用于接收器噪声方差不均匀的情况。文中包含了一些数值模拟结果，以显示所开发的设计算法带来的好处。
{"title":"Optimal Placement of the Receivers for Multistatic Target Localization","authors":"Ghania Fatima;Petre Stoica;Augusto Aubry;Antonio De Maio;Prabhu Babu","doi":"10.1109/TRS.2024.3382956","DOIUrl":"https://doi.org/10.1109/TRS.2024.3382956","url":null,"abstract":"In this paper, we propose a numerical method for the optimal placement of the receivers in a multistatic target localization system (with a single transmitter and multiple receivers) in order to improve the achievable target estimation accuracy of time-sum-of-arrival (TSOA) localization techniques, for 2D and 3D scenarios. The proposed algorithm is based on the principle of block majorization minimization (block MM) which is a combination of block coordinate descent and majorization-minimization (MM) methods. More precisely, we formulate the design objective for the placement of sensors performing TSOA measurements using \u0000<inline-formula> <tex-math>$A-$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$D-$ </tex-math></inline-formula>\u0000 optimality criteria, and propose an iterative algorithm to find the optimal solution by first splitting the design variable into M blocks (where M is the number of receivers) and then applying the principle of MM on each block. The proposed method can additionally handle the cases where the transmitter also acts as a receiver. The framework can also be applied to the case of non-uniform noise variances at the receivers. Several numerical simulation results are included to show the benefits offered by the developed design algorithm.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"391-403"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140537399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-22DOI: 10.1109/TRS.2024.3381033
Abigael Taylor;Olivier Rabaste
This paper considers two optimization problems for the conception of a co-located Multiple Input Multiple Output Dual-Functional Radar-Communication system. The first one consists in transmitting desired waveforms in a few specific directions, while minimizing the energy on other angular intervals. This enables to guarantee a certain discretion of the system. The second one examines the problem of synthesizing robust waveforms, so that their performance remain stable on the whole mainlobe (and not just on the sole direction of interest). The two problems involve the use of the Discrete Prolates Spheroidal Sequences, and are solved using gradient algorithms. The performance of the proposed methods are evaluated on simulations, by examining the radiated beampattern, as well as the quality of the waveforms.
{"title":"Let It GUWO: Waveform Optimization for Angular Blanking and Robustification in a MIMO Dual-Functional Radar-Communication System","authors":"Abigael Taylor;Olivier Rabaste","doi":"10.1109/TRS.2024.3381033","DOIUrl":"https://doi.org/10.1109/TRS.2024.3381033","url":null,"abstract":"This paper considers two optimization problems for the conception of a co-located Multiple Input Multiple Output Dual-Functional Radar-Communication system. The first one consists in transmitting desired waveforms in a few specific directions, while minimizing the energy on other angular intervals. This enables to guarantee a certain discretion of the system. The second one examines the problem of synthesizing robust waveforms, so that their performance remain stable on the whole mainlobe (and not just on the sole direction of interest). The two problems involve the use of the Discrete Prolates Spheroidal Sequences, and are solved using gradient algorithms. The performance of the proposed methods are evaluated on simulations, by examining the radiated beampattern, as well as the quality of the waveforms.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"380-390"},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-21DOI: 10.1109/TRS.2024.3396968
Shannon D. Blunt;Martin Vossiek;Fabiola Colone
The utility of radar imaging [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] spans a variety of different use-cases including scientific remote sensing, medical diagnostic tools, high-resolution security screening, automotive sensing, weather radar, and numerous defense applications. For example, synthetic aperture radar (SAR) permits long-range imaging intelligence, surveillance, and reconnaissance (ISR) in all-weather environments and enables ground-penetrating imaging for archeology and glaciology. Micro-Doppler features derived by short-time Fourier transform (STFT) imaging facilitate the assessment of cyclic features such as spinning rotors on aircraft and providing a mechanism whereby sign language can be made machine-readable. Imaging derived from polarimetric weather radars can discriminate the type of precipitation as a function of geographic location. Automotive radar research likewise explores the imaging capabilities to aid in collision avoidance. Finally, inverse SAR (ISAR) leverages the motion of an illuminated object to construct imagery for identification and discrimination. Following another strong response, this third special section of the recently launched IEEE Transactions on Radar Systems comprises eight selected papers that explore different aspects of imaging across the radar research community.
雷达成像的用途[1]、[2]、[3]、[4]、[5]、[6]、[7]、[8]、[9] 广泛，包括科学遥感、医疗诊断工具、高分辨率安全检查、汽车传感、气象雷达和众多国防应用。例如，合成孔径雷达（SAR）允许在全天候环境中进行长距离成像情报、监视和侦察（ISR），并能为考古学和冰川学提供地面穿透成像。通过短时傅立叶变换（STFT）成像技术获得的微多普勒特征有助于评估飞机上旋转的旋翼等周期性特征，并提供了一种使机器可读取手语的机制。从偏振天气雷达获得的成像可以根据地理位置区分降水类型。汽车雷达研究同样也在探索帮助避免碰撞的成像功能。最后，反向合成孔径雷达（ISAR）利用被照物体的运动来构建图像，以进行识别和分辨。由于反响强烈，最近推出的《电气和电子工程师学会雷达系统论文集》（IEEE Transactions on Radar Systems）的第三个特别栏目精选了八篇论文，探讨雷达研究领域成像的不同方面。
{"title":"Foreword to the Special section on Advances in Radar Imaging","authors":"Shannon D. Blunt;Martin Vossiek;Fabiola Colone","doi":"10.1109/TRS.2024.3396968","DOIUrl":"https://doi.org/10.1109/TRS.2024.3396968","url":null,"abstract":"The utility of radar imaging \u0000<xref>[1]</xref>\u0000, \u0000<xref>[2]</xref>\u0000, \u0000<xref>[3]</xref>\u0000, \u0000<xref>[4]</xref>\u0000, \u0000<xref>[5]</xref>\u0000, \u0000<xref>[6]</xref>\u0000, \u0000<xref>[7]</xref>\u0000, \u0000<xref>[8]</xref>\u0000, \u0000<xref>[9]</xref>\u0000 spans a variety of different use-cases including scientific remote sensing, medical diagnostic tools, high-resolution security screening, automotive sensing, weather radar, and numerous defense applications. For example, synthetic aperture radar (SAR) permits long-range imaging intelligence, surveillance, and reconnaissance (ISR) in all-weather environments and enables ground-penetrating imaging for archeology and glaciology. Micro-Doppler features derived by short-time Fourier transform (STFT) imaging facilitate the assessment of cyclic features such as spinning rotors on aircraft and providing a mechanism whereby sign language can be made machine-readable. Imaging derived from polarimetric weather radars can discriminate the type of precipitation as a function of geographic location. Automotive radar research likewise explores the imaging capabilities to aid in collision avoidance. Finally, inverse SAR (ISAR) leverages the motion of an illuminated object to construct imagery for identification and discrimination. Following another strong response, this third special section of the recently launched IEEE Transactions on Radar Systems comprises eight selected papers that explore different aspects of imaging across the radar research community.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"482-483"},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-19DOI: 10.1109/TRS.2024.3378119
Ritesh Kumar Sharma;Dipanwita Roy Chowdhury;Jolly Dhar;Umang Bhatia;Jaimin Tanna;Shivani Bhargav;Swastik Saini;Priyanka;B. Saravana Kumar;Ch. V. Narasimha Rao
Accurate nadir altitude information is a critical requirement for the precise and safe landing of an autonomously guided vehicle. Radar altimeter (RA) is well suited to provide precise height Above Ground Level (AGL) under the worst environmental conditions viz engine plumes, dust, cloud-covers, etc. The problem of precise nadir altitude estimation using a single RA system configuration under a wide range and attitude variation of ±25° is a challenging and open issue. The wide antenna beam is necessary to get adequate return power from the ground to measure the altitude under the influence of a wide range of attitude variations. Under the circumstances, the major problem of the existing methods for long-range measurement along with wide attitude variation is that they easily lose the current altitude value or require a long tracking time. This paper presents the design of a novel robust RA-processing algorithm, the design and implementation of high-performance processors, architecture details, and salient performance features. This digital Radar Altimeter’s high throughput processor performance has been validated via extensive lab tests and field tests and achieved excellent performance during the actual Landing of ISRO’s autonomous Re-usable Launch Vehicle (RLV) mission under wide attitude variation.
准确的天底高度信息是自动制导飞行器精确安全着陆的关键要求。雷达高度计（RA）非常适合在最恶劣的环境条件下提供精确的地面高度（AGL），如发动机烟羽、灰尘、云层等。在±25°的大范围姿态变化下，使用单一 RA 系统配置进行精确的天底高度估计是一个具有挑战性的未决问题。宽天线波束是在大范围姿态变化影响下从地面获得足够回波功率以测量高度的必要条件。在这种情况下，现有的大范围姿态变化远距离测量方法的主要问题是容易丢失当前高度值或需要较长的跟踪时间。本文介绍了一种新型鲁棒性雷达高度计处理算法的设计、高性能处理器的设计与实现、结构细节以及突出的性能特点。该数字雷达高度计的高吞吐量处理器性能已通过大量实验室测试和现场测试进行了验证，并在姿态变化较大的情况下，在国际空间研究组织自主可重复使用运载火箭（RLV）的实际着陆任务中取得了优异的性能。
{"title":"Robust Radar Altimeter Processor","authors":"Ritesh Kumar Sharma;Dipanwita Roy Chowdhury;Jolly Dhar;Umang Bhatia;Jaimin Tanna;Shivani Bhargav;Swastik Saini;Priyanka;B. Saravana Kumar;Ch. V. Narasimha Rao","doi":"10.1109/TRS.2024.3378119","DOIUrl":"https://doi.org/10.1109/TRS.2024.3378119","url":null,"abstract":"Accurate nadir altitude information is a critical requirement for the precise and safe landing of an autonomously guided vehicle. Radar altimeter (RA) is well suited to provide precise height Above Ground Level (AGL) under the worst environmental conditions viz engine plumes, dust, cloud-covers, etc. The problem of precise nadir altitude estimation using a single RA system configuration under a wide range and attitude variation of ±25° is a challenging and open issue. The wide antenna beam is necessary to get adequate return power from the ground to measure the altitude under the influence of a wide range of attitude variations. Under the circumstances, the major problem of the existing methods for long-range measurement along with wide attitude variation is that they easily lose the current altitude value or require a long tracking time. This paper presents the design of a novel robust RA-processing algorithm, the design and implementation of high-performance processors, architecture details, and salient performance features. This digital Radar Altimeter’s high throughput processor performance has been validated via extensive lab tests and field tests and achieved excellent performance during the actual Landing of ISRO’s autonomous Re-usable Launch Vehicle (RLV) mission under wide attitude variation.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"372-379"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.1109/TRS.2024.3378125
Ping Li;Bang Huang;Wen-Qin Wang
For Frequency diverse array multiple-input multiple-out (FDA-MIMO) radar, this paper studies the knowledge-aided Bayesian detection for a one-range-bin distributed target with multiple scatters operating in Gaussian clutter environment with unknown and stochastic clutter covariance matrix. Specifically, we build the FDA-MIMO receive signal model by capitalizing on orthogonality in the frequency domain. Subsequently, an inverse complex Wishart distribution is assigned to the clutter covariance matrix for mathematical tractability, serving as knowledge-aided information. With free training data, two adaptive detectors are introduced by leveraging the Bayesian framework, based on Rao and Wald criteria, namely, Bayesian Rao (BRao) and Bayesian Wald (BWald), respectively. Notably, it is essential to highlight that the received FDA-MIMO signals can be directly applied to adaptive detectors without needing matched filtering. The simulation results confirm that, in the case of signal matching, the BWald can provide detection performance comparable to that of the existing BGLRT. Additionally, when facing mismatched signals, the proposed BWald and BRao detectors demonstrate stronger robustness and selectivity capabilities.
{"title":"Knowledge-Aided Bayesian Detection of Distributed Target for FDA-MIMO Radar in Gaussian Clutter","authors":"Ping Li;Bang Huang;Wen-Qin Wang","doi":"10.1109/TRS.2024.3378125","DOIUrl":"https://doi.org/10.1109/TRS.2024.3378125","url":null,"abstract":"For Frequency diverse array multiple-input multiple-out (FDA-MIMO) radar, this paper studies the knowledge-aided Bayesian detection for a one-range-bin distributed target with multiple scatters operating in Gaussian clutter environment with unknown and stochastic clutter covariance matrix. Specifically, we build the FDA-MIMO receive signal model by capitalizing on orthogonality in the frequency domain. Subsequently, an inverse complex Wishart distribution is assigned to the clutter covariance matrix for mathematical tractability, serving as knowledge-aided information. With free training data, two adaptive detectors are introduced by leveraging the Bayesian framework, based on Rao and Wald criteria, namely, Bayesian Rao (BRao) and Bayesian Wald (BWald), respectively. Notably, it is essential to highlight that the received FDA-MIMO signals can be directly applied to adaptive detectors without needing matched filtering. The simulation results confirm that, in the case of signal matching, the BWald can provide detection performance comparable to that of the existing BGLRT. Additionally, when facing mismatched signals, the proposed BWald and BRao detectors demonstrate stronger robustness and selectivity capabilities.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"344-354"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140192050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.1109/TRS.2024.3378123
Aleksanteri B. Vattulainen;Samiur Rahman;Duncan A. Robertson
Radar systems operating within the 220 GHz atmospheric transmission window are comparatively rare despite the benefits they offer in high angular, range, and Doppler resolutions. Given the growing availability of solid-state signal generation components designed for this frequency range, interest in the sensing potential of this region is increasing. This paper presents the development and characterization of ‘Theseus’, a 207 GHz FMCW Doppler radar designed for sea clutter and marine target characterization but also capable of a large variety of other close-range environmental sensing uses. The radar carrier frequency is tunable between 200-208 GHz with a maximum chirp bandwidth of 2 GHz resulting in a range resolution of 7.5 cm, and a chirp repetition interval (CRI) of $67.59~mu text{s}$ giving a maximum unambiguous velocity of $mathbf {pm {}5.36}$ ms $^{-1}$ . Several measurement application examples are presented, showcasing a wealth of micro-Doppler and micro-range information gathered from a variety of targets and clutter including sea clutter, humans swimming and running, UAV flight, a plan position indicator (PPI) scan of a terrestrial environment, and rain clutter. Data in this frequency band are very rare in the open literature, and thus the high range and Doppler resolution measurement capabilities of this radar present an opportunity to expand the knowledge in this area.
{"title":"G-Band FMCW Doppler Radar for Close-Range Environmental Sensing","authors":"Aleksanteri B. Vattulainen;Samiur Rahman;Duncan A. Robertson","doi":"10.1109/TRS.2024.3378123","DOIUrl":"https://doi.org/10.1109/TRS.2024.3378123","url":null,"abstract":"Radar systems operating within the 220 GHz atmospheric transmission window are comparatively rare despite the benefits they offer in high angular, range, and Doppler resolutions. Given the growing availability of solid-state signal generation components designed for this frequency range, interest in the sensing potential of this region is increasing. This paper presents the development and characterization of ‘Theseus’, a 207 GHz FMCW Doppler radar designed for sea clutter and marine target characterization but also capable of a large variety of other close-range environmental sensing uses. The radar carrier frequency is tunable between 200-208 GHz with a maximum chirp bandwidth of 2 GHz resulting in a range resolution of 7.5 cm, and a chirp repetition interval (CRI) of \u0000<inline-formula> <tex-math>$67.59~mu text{s}$ </tex-math></inline-formula>\u0000 giving a maximum unambiguous velocity of \u0000<inline-formula> <tex-math>$mathbf {pm {}5.36}$ </tex-math></inline-formula>\u0000 ms\u0000<inline-formula> <tex-math>$^{-1}$ </tex-math></inline-formula>\u0000. Several measurement application examples are presented, showcasing a wealth of micro-Doppler and micro-range information gathered from a variety of targets and clutter including sea clutter, humans swimming and running, UAV flight, a plan position indicator (PPI) scan of a terrestrial environment, and rain clutter. Data in this frequency band are very rare in the open literature, and thus the high range and Doppler resolution measurement capabilities of this radar present an opportunity to expand the knowledge in this area.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"355-371"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10473101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-17DOI: 10.1109/TRS.2024.3402498
Amir Zaimbashi;Maria Sabrina Greco;Fulvio Gini
This article addresses the problem of target detection in a two-channel distributed multiple-input-multiple-output (MIMO) passive radar (PR). In this scenario, multiple distributed transmitters emit signals received by two-channel distributed receivers, with one channel for surveillance and the other for reference. To address this PR detection problem, we propose two detectors based on the likelihood ratio test (LRT) principle. One is a subspace-based detector, where the unknown interference subspace is estimated. In this detector, only surveillance channels (SCs) are used to formulate the detection problem as a binary composite hypothesis-testing problem. However, reference channels (RCs) are employed to estimate the unknown interference subspace, resulting in a two-step detection method with a single-channel formulation. The second detector is an eigenvalue-based detector that utilizes both SCs and RCs to formulate the target detection problem. In addition, we present a low-complexity implementation of the eigenvalue-based detector, making it suitable for short- to long-range PR scenarios. Simulation results demonstrate the effectiveness of the proposed detector in target detection and interference cancellation capabilities.
{"title":"Low-Complexity LRT-Based Passive Radar Target Detection Algorithm in Interfering Conditions","authors":"Amir Zaimbashi;Maria Sabrina Greco;Fulvio Gini","doi":"10.1109/TRS.2024.3402498","DOIUrl":"https://doi.org/10.1109/TRS.2024.3402498","url":null,"abstract":"This article addresses the problem of target detection in a two-channel distributed multiple-input-multiple-output (MIMO) passive radar (PR). In this scenario, multiple distributed transmitters emit signals received by two-channel distributed receivers, with one channel for surveillance and the other for reference. To address this PR detection problem, we propose two detectors based on the likelihood ratio test (LRT) principle. One is a subspace-based detector, where the unknown interference subspace is estimated. In this detector, only surveillance channels (SCs) are used to formulate the detection problem as a binary composite hypothesis-testing problem. However, reference channels (RCs) are employed to estimate the unknown interference subspace, resulting in a two-step detection method with a single-channel formulation. The second detector is an eigenvalue-based detector that utilizes both SCs and RCs to formulate the target detection problem. In addition, we present a low-complexity implementation of the eigenvalue-based detector, making it suitable for short- to long-range PR scenarios. Simulation results demonstrate the effectiveness of the proposed detector in target detection and interference cancellation capabilities.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"549-560"},"PeriodicalIF":0.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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