The recent deployment of 5G technology in the C band has raised concerns regarding potential interference with aeronautical radar altimeters. The 5G systems in the C band operate within a frequency range of 3.7–3.98 GHz, which closely aligns with the operational frequency of radar altimeters, falling within the range of 4.2–4.4 GHz. This proximity in operational frequencies increases the possibility of interference between the two systems. In this article, we explore two primary objectives: first, to examine the potential for interference between the 5G C band and radar altimeters, and second, to develop techniques for mitigating this interference. To achieve these objectives, we assess interference in a real-world scenario, where multiple base stations (BSs) are deployed to serve an operational runway. In addition, two interference management techniques were proposed and evaluated within the assessed real-life scenario. The first involves the implementation of adaptive BS using the power control (PC) method, which aims to mitigate interference with minimal impact on coverage by adjusting the transmitting power for the BS that contributes the most to the interference model. A modification to this technique was applied to loop over the coverage areas instead of individual BSs. This technique is useful in scenarios, where BSs are implemented close to each other with overlapping coverage. Finally, a sequential quadratic programming (SQP) optimization algorithm was developed to optimize the locations of BSs, minimizing interference while maintaining coverage. This work has explored the impact of potential interference between 5G in the C band and radar altimeters and suggested practical methods to allow the coexistence of both systems, thereby ensuring aviation safety and fulfilling the telecommunication sector’s objectives.
{"title":"Assessment and Mitigation Approaches of 5G C-Band Interference With Aeronautical Radar Altimeter","authors":"Aisha Elsayem;Ali Massoud;Haidy Elghamrawy;Aboelmagd Noureldin","doi":"10.1109/TRS.2025.3557219","DOIUrl":"https://doi.org/10.1109/TRS.2025.3557219","url":null,"abstract":"The recent deployment of 5G technology in the C band has raised concerns regarding potential interference with aeronautical radar altimeters. The 5G systems in the C band operate within a frequency range of 3.7–3.98 GHz, which closely aligns with the operational frequency of radar altimeters, falling within the range of 4.2–4.4 GHz. This proximity in operational frequencies increases the possibility of interference between the two systems. In this article, we explore two primary objectives: first, to examine the potential for interference between the 5G C band and radar altimeters, and second, to develop techniques for mitigating this interference. To achieve these objectives, we assess interference in a real-world scenario, where multiple base stations (BSs) are deployed to serve an operational runway. In addition, two interference management techniques were proposed and evaluated within the assessed real-life scenario. The first involves the implementation of adaptive BS using the power control (PC) method, which aims to mitigate interference with minimal impact on coverage by adjusting the transmitting power for the BS that contributes the most to the interference model. A modification to this technique was applied to loop over the coverage areas instead of individual BSs. This technique is useful in scenarios, where BSs are implemented close to each other with overlapping coverage. Finally, a sequential quadratic programming (SQP) optimization algorithm was developed to optimize the locations of BSs, minimizing interference while maintaining coverage. This work has explored the impact of potential interference between 5G in the C band and radar altimeters and suggested practical methods to allow the coexistence of both systems, thereby ensuring aviation safety and fulfilling the telecommunication sector’s objectives.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"615-629"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875168","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 : 2025-03-31DOI: 10.1109/TRS.2025.3556323
Gruffudd Jones;Morgan Coe;Lily Beesley;Leah-Nani Alconcel;Marco Martorella;Marina Gashinova
This article is concerned with the investigation and analysis of a new operational and technical capability to assess geosynchronous orbit (GEO) satellites from spaceborne platforms using extremely high-frequency radar operating at sub-THz frequencies. The concept of close monitoring and highly detailed imagery of GEO assets from all aspects, including those unattainable from the Earth, is developed based on the analysis of two proposed orbital deployment scenarios. Accounting for orbital perturbation factors during an extended period of time, the ability to build multiaspect ISAR imagery of the asset during single and multiple encounters is demonstrated, based on the mutual attitudes of the asset and the radar platform. A linearized model of the encounter geometry is presented and the approach to generate a sequence of ISAR image frames according to the geometry of the proposed scenarios is detailed. The simulation of ISAR frames at two frequency bands, centered at 75 and 300 GHz produced in a developed metaheuristic simulator, graphical electromagnetic ISAR simulator for sub-THz (GEIST), is demonstrated, to highlight the transition of scattering mechanisms and the change in visibility of particular features. Attitude-agnostic frame-to-frame image alignment and linear feature extraction using the Hough transform are then demonstrated on a sequence of simulated images.
{"title":"Strategies for Monitoring of Assets in Geosynchronous Orbit (GEO) Using Space-Based Sub-THz Inverse Synthetic Aperture Radar (ISAR)","authors":"Gruffudd Jones;Morgan Coe;Lily Beesley;Leah-Nani Alconcel;Marco Martorella;Marina Gashinova","doi":"10.1109/TRS.2025.3556323","DOIUrl":"https://doi.org/10.1109/TRS.2025.3556323","url":null,"abstract":"This article is concerned with the investigation and analysis of a new operational and technical capability to assess geosynchronous orbit (GEO) satellites from spaceborne platforms using extremely high-frequency radar operating at sub-THz frequencies. The concept of close monitoring and highly detailed imagery of GEO assets from all aspects, including those unattainable from the Earth, is developed based on the analysis of two proposed orbital deployment scenarios. Accounting for orbital perturbation factors during an extended period of time, the ability to build multiaspect ISAR imagery of the asset during single and multiple encounters is demonstrated, based on the mutual attitudes of the asset and the radar platform. A linearized model of the encounter geometry is presented and the approach to generate a sequence of ISAR image frames according to the geometry of the proposed scenarios is detailed. The simulation of ISAR frames at two frequency bands, centered at 75 and 300 GHz produced in a developed metaheuristic simulator, graphical electromagnetic ISAR simulator for sub-THz (GEIST), is demonstrated, to highlight the transition of scattering mechanisms and the change in visibility of particular features. Attitude-agnostic frame-to-frame image alignment and linear feature extraction using the Hough transform are then demonstrated on a sequence of simulated images.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"656-667"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929683","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 : 2025-03-30DOI: 10.1109/TRS.2025.3575167
He Zhou;Jianxin Wu
To address the challenges of poor detection robustness caused by angle scintillation and the inability to achieve effective coherent accumulation in the range domain for ultrawideband (UWB) radar extended targets, this article proposes a novel single-pulse range-domain coherent accumulation method for UWB extended targets. First, the full-bandwidth signal model is approximated and converted into a fully digital array model. When full-bandwidth conditions are not met, the wideband target’s radar cross section (RCS) scattering centers are transformed into the subarray domain. The original target’s RCS phase and amplitude are reconstructed through the subarray, and phase modulation is used to adjust the beam direction, enabling a scan over the observed angles and achieving high gain on the target to obtain the maximum RCS value. Subsequently, digital beamforming (DBF) is applied to the target’s wideband range profile data to complete coherent accumulation in the range domain.
{"title":"An Ultrawideband Radar Target Range-Domain Coherent Accumulation Method","authors":"He Zhou;Jianxin Wu","doi":"10.1109/TRS.2025.3575167","DOIUrl":"https://doi.org/10.1109/TRS.2025.3575167","url":null,"abstract":"To address the challenges of poor detection robustness caused by angle scintillation and the inability to achieve effective coherent accumulation in the range domain for ultrawideband (UWB) radar extended targets, this article proposes a novel single-pulse range-domain coherent accumulation method for UWB extended targets. First, the full-bandwidth signal model is approximated and converted into a fully digital array model. When full-bandwidth conditions are not met, the wideband target’s radar cross section (RCS) scattering centers are transformed into the subarray domain. The original target’s RCS phase and amplitude are reconstructed through the subarray, and phase modulation is used to adjust the beam direction, enabling a scan over the observed angles and achieving high gain on the target to obtain the maximum RCS value. Subsequently, digital beamforming (DBF) is applied to the target’s wideband range profile data to complete coherent accumulation in the range domain.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"852-863"},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323190","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 : 2025-03-30DOI: 10.1109/TRS.2025.3574010
S. Hamed Javadi;André Bourdoux;Adnan Albaba;Hichem Sahli
Radars provide robust perception of vehicle surroundings by effectively functioning in poor light and adverse weather conditions. Synthetic aperture radar (SAR) algorithms are used to address the limited angular resolution of radars by enlarging antenna aperture size synthetically as the radar moves. An autofocus algorithm is essential to improve the SAR image quality by compensating for errors mainly caused by inaccurate radar localization. Existing autofocus algorithms are mostly tailored for the frequency-domain SAR techniques which are prevalent in aviation and spaceborne applications, thanks to their lower complexity in large data processing. However, in the automotive context, the backprojection algorithm (BPA) is often preferred since it provides less distorted images at the cost of more complexity. Addressing the gap in efficient autofocus solutions for time-domain algorithms, this article introduces a dual-layered autofocus strategy that integrates the polar format algorithm (PFA) with BPA. The first layer uses a novel localization error compensation autofocus (LECA) processing pipeline to estimate and correct the localization errors within the PFA domain, leveraging its computational efficiency. The second layer seamlessly transfers these corrections to BPA, enabling high-quality SAR imaging while maintaining low complexity. In addition, the strategy extends phase gradient autofocus (PGA) techniques to enhance the efficiency of localization error compensation for BPA. Validated through real-world automotive experiments, the proposed pipeline delivers state-of-the-art image focus and resolution, setting a new benchmark for computationally efficient SAR imaging.
{"title":"A Low-Complexity PFA-Based Autofocus Algorithm for Automotive SAR","authors":"S. Hamed Javadi;André Bourdoux;Adnan Albaba;Hichem Sahli","doi":"10.1109/TRS.2025.3574010","DOIUrl":"https://doi.org/10.1109/TRS.2025.3574010","url":null,"abstract":"Radars provide robust perception of vehicle surroundings by effectively functioning in poor light and adverse weather conditions. Synthetic aperture radar (SAR) algorithms are used to address the limited angular resolution of radars by enlarging antenna aperture size synthetically as the radar moves. An autofocus algorithm is essential to improve the SAR image quality by compensating for errors mainly caused by inaccurate radar localization. Existing autofocus algorithms are mostly tailored for the frequency-domain SAR techniques which are prevalent in aviation and spaceborne applications, thanks to their lower complexity in large data processing. However, in the automotive context, the backprojection algorithm (BPA) is often preferred since it provides less distorted images at the cost of more complexity. Addressing the gap in efficient autofocus solutions for time-domain algorithms, this article introduces a dual-layered autofocus strategy that integrates the polar format algorithm (PFA) with BPA. The first layer uses a novel localization error compensation autofocus (LECA) processing pipeline to estimate and correct the localization errors within the PFA domain, leveraging its computational efficiency. The second layer seamlessly transfers these corrections to BPA, enabling high-quality SAR imaging while maintaining low complexity. In addition, the strategy extends phase gradient autofocus (PGA) techniques to enhance the efficiency of localization error compensation for BPA. Validated through real-world automotive experiments, the proposed pipeline delivers state-of-the-art image focus and resolution, setting a new benchmark for computationally efficient SAR imaging.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"799-810"},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308501","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 : 2025-03-28DOI: 10.1109/TRS.2025.3574571
Yun Ge;Yiyu Wang;Gen Li;Ruoyi Wang;Qingwu Chen;Gang Wang
The ghost echoes in radar detection of a subject behaving in a nonline-of-sight (NLOS) environment can be utilized to benefit behavior recognition. Different echoes carry unique feature information due to different multipath wave incidents and scattering directions in NLOS radar detection. By fusing the ghost echo information, the recognition of subject postures behaving in the NLOS region can be enhanced. To suppress the effects of dynamic multipath noise and ensure feature extraction from as many echoes as possible, a denoising algorithm is proposed based on frequency segregation and probability estimation (FSaPE) of the time-frequency (TF) images of human behavior. To fuse the features extracted from many echoes, a multipath-based multistage input convolutional neural network (MBMI-CNN) is proposed and trained. The scheme is demonstrated by detecting people behaving behind an L-shaped corner with 77-GHz linear frequency-modulated continuous wave (FMCW) radar. It is shown that six typical postures behaving behind the corner can be successfully classified, with an average classification accuracy of 99.17% for all the postures.
{"title":"Multipath Feature Expansion for Detection of Human Behaviors in NLOS Region Using mmWave Radar","authors":"Yun Ge;Yiyu Wang;Gen Li;Ruoyi Wang;Qingwu Chen;Gang Wang","doi":"10.1109/TRS.2025.3574571","DOIUrl":"https://doi.org/10.1109/TRS.2025.3574571","url":null,"abstract":"The ghost echoes in radar detection of a subject behaving in a nonline-of-sight (NLOS) environment can be utilized to benefit behavior recognition. Different echoes carry unique feature information due to different multipath wave incidents and scattering directions in NLOS radar detection. By fusing the ghost echo information, the recognition of subject postures behaving in the NLOS region can be enhanced. To suppress the effects of dynamic multipath noise and ensure feature extraction from as many echoes as possible, a denoising algorithm is proposed based on frequency segregation and probability estimation (FSaPE) of the time-frequency (TF) images of human behavior. To fuse the features extracted from many echoes, a multipath-based multistage input convolutional neural network (MBMI-CNN) is proposed and trained. The scheme is demonstrated by detecting people behaving behind an L-shaped corner with 77-GHz linear frequency-modulated continuous wave (FMCW) radar. It is shown that six typical postures behaving behind the corner can be successfully classified, with an average classification accuracy of 99.17% for all the postures.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"864-874"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557811","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 : 2025-03-24DOI: 10.1109/TRS.2025.3554198
Ruilin Chen;Shisheng Guo;Jiahui Chen;Xingyu Gu;Guolong Cui;Lingjiang Kong;Weijian Liu
Direct position determination (DPD) for multiple targets in distributed multiple-input multiple-output (MIMO) radar has been a challenging problem. This article proposed a low-complexity multitarget detection and localization method for distributed MIMO radar. To address the problem of exponential expansion of the state space caused by high-dimensional detection in traditional DPD, a low-dimensional detector is proposed. Specifically, we divide the radar-sensed scene into discrete 2-D grid cells and derive the maximum likelihood estimation (MLE) function as well as the generalized likelihood ratio test (GLRT) detector in the 2-D scene. In addition, the probability of a false alarm (PFA) for the derived GLRT detector has an analytic solution, ensuring each grid cell maintains a constant PFA. Since the proposed detector introduces a large number of false targets, we further propose the clean with protected cells (CPCs) algorithm to remove false targets and localize real targets. This method generates protection points based on the relationship between the real targets and the radar channels, achieving high-accuracy localization with low computational complexity, even in scenes with inseparable targets. Finally, both numerical simulations and experimental data demonstrate the effectiveness of the proposed method. Simulation results show that the proposed method achieves the best detection performance compared to state-of-the-art methods, with an average processing time of only 565.7 ms, meeting the requirements for real-time target detection and localization.
分布式多输入多输出（MIMO）雷达中多目标的直接定位（DPD）一直是一个具有挑战性的问题。针对分布式MIMO雷达，提出了一种低复杂度的多目标检测与定位方法。针对传统DPD中由于高维检测导致状态空间呈指数扩展的问题，提出了一种低维检测器。具体而言，我们将雷达感测场景划分为离散的二维网格单元，并推导出二维场景中的最大似然估计（MLE）函数和广义似然比检验（GLRT）检测器。此外，导出的GLRT检测器的虚警概率（PFA）具有解析解，确保每个网格单元保持恒定的PFA。由于该检测器引入了大量假目标，我们进一步提出了CPCs （clean with protection cells）算法来去除假目标并定位真实目标。该方法根据真实目标与雷达通道之间的关系生成保护点，即使在目标不可分割的场景下，也能以较低的计算复杂度实现高精度定位。最后，通过数值模拟和实验数据验证了该方法的有效性。仿真结果表明，与现有方法相比，该方法具有最佳的检测性能，平均处理时间仅为565.7 ms，满足实时目标检测和定位的要求。
{"title":"Low-Complexity Multitarget Detection and Localization Method for Distributed MIMO Radar","authors":"Ruilin Chen;Shisheng Guo;Jiahui Chen;Xingyu Gu;Guolong Cui;Lingjiang Kong;Weijian Liu","doi":"10.1109/TRS.2025.3554198","DOIUrl":"https://doi.org/10.1109/TRS.2025.3554198","url":null,"abstract":"Direct position determination (DPD) for multiple targets in distributed multiple-input multiple-output (MIMO) radar has been a challenging problem. This article proposed a low-complexity multitarget detection and localization method for distributed MIMO radar. To address the problem of exponential expansion of the state space caused by high-dimensional detection in traditional DPD, a low-dimensional detector is proposed. Specifically, we divide the radar-sensed scene into discrete 2-D grid cells and derive the maximum likelihood estimation (MLE) function as well as the generalized likelihood ratio test (GLRT) detector in the 2-D scene. In addition, the probability of a false alarm (PFA) for the derived GLRT detector has an analytic solution, ensuring each grid cell maintains a constant PFA. Since the proposed detector introduces a large number of false targets, we further propose the clean with protected cells (CPCs) algorithm to remove false targets and localize real targets. This method generates protection points based on the relationship between the real targets and the radar channels, achieving high-accuracy localization with low computational complexity, even in scenes with inseparable targets. Finally, both numerical simulations and experimental data demonstrate the effectiveness of the proposed method. Simulation results show that the proposed method achieves the best detection performance compared to state-of-the-art methods, with an average processing time of only 565.7 ms, meeting the requirements for real-time target detection and localization.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"599-614"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821236","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}
In this article, we present a compact software-defined ultrawideband (UWB) 0.4–8.3-GHz transmitter that utilizes a nonlinear transmission line (NLTL) to expand the frequency of a transmitted pulse from a low-cost 2.5-GHz bandwidth arbitrary waveform generator (AWG). The developed transmitter consists of an AWG, amplification boards, and an NLTL. By leveraging the software-defined capabilities of the AWG and applying a digital predistortion (DPD) algorithm, we can iteratively adjust the input pulse to fine-tune and optimize the output pulse bandwidth. Ultimately, the UWB transmitter can generate software-defined pulses up to 8.3 GHz and detect 0.25-mm surface objects with a 3-dB area of 1.4 cm.
{"title":"A Low-Cost and Compact Software-Defined UWB Transmitter for Radar Utilizing a Nonlinear Transmission Line","authors":"Tyler Kelley;Stephen Pancrazio;Samuel Wagner;Ababil Hossain;Nhat Tran;Anh-Vu Pham","doi":"10.1109/TRS.2025.3554135","DOIUrl":"https://doi.org/10.1109/TRS.2025.3554135","url":null,"abstract":"In this article, we present a compact software-defined ultrawideband (UWB) 0.4–8.3-GHz transmitter that utilizes a nonlinear transmission line (NLTL) to expand the frequency of a transmitted pulse from a low-cost 2.5-GHz bandwidth arbitrary waveform generator (AWG). The developed transmitter consists of an AWG, amplification boards, and an NLTL. By leveraging the software-defined capabilities of the AWG and applying a digital predistortion (DPD) algorithm, we can iteratively adjust the input pulse to fine-tune and optimize the output pulse bandwidth. Ultimately, the UWB transmitter can generate software-defined pulses up to 8.3 GHz and detect 0.25-mm surface objects with a 3-dB area of 1.4 cm.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"591-598"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801012","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 : 2025-03-20DOI: 10.1109/TRS.2025.3553116
Zhengyang Sun;Liwu Wen;Jinshan Ding
Video synthetic aperture radar (ViSAR) is a promising technology for the surveillance of ground-moving targets. Traditionally, ViSAR imaging and moving target tracking are performed sequentially, where high-frame-rate imaging is applied to the entire SAR scene. However, this approach generates redundant information that is often unnecessary for ViSAR applications. We propose a partitioned adaptive frame-rate (PAFR) ViSAR processing strategy, which adaptively partitions the SAR scene, applying high-frame-rate imaging to potential target regions and low frame rate to large static areas. An integrated imaging and tracking algorithm that synthesizes back-projection (BP) and track-before-detect (TBD) techniques has been derived for efficient bidirectional information exchange. BP imaging provides high-resolution measurements to refine tracking parameters, while TBD tracking offers predictive data to guide the adaptive partitioning of the imaging area. Additionally, we enhance the traditional dynamic programming-based TBD (DP-TBD) algorithm by incorporating the morphological features of target shadows, allowing for more accurate corrections and refinements of predicted states. This enhancement significantly improves both tracking accuracy and speed. The experimental results from airborne radar data have proven the capability of the proposed algorithm to achieve both efficient PAFR imaging and fast target tracking simultaneously, which paves the way for more potential applications in ViSAR.
{"title":"Adaptive Frame-Rate Partitioned Video SAR","authors":"Zhengyang Sun;Liwu Wen;Jinshan Ding","doi":"10.1109/TRS.2025.3553116","DOIUrl":"https://doi.org/10.1109/TRS.2025.3553116","url":null,"abstract":"Video synthetic aperture radar (ViSAR) is a promising technology for the surveillance of ground-moving targets. Traditionally, ViSAR imaging and moving target tracking are performed sequentially, where high-frame-rate imaging is applied to the entire SAR scene. However, this approach generates redundant information that is often unnecessary for ViSAR applications. We propose a partitioned adaptive frame-rate (PAFR) ViSAR processing strategy, which adaptively partitions the SAR scene, applying high-frame-rate imaging to potential target regions and low frame rate to large static areas. An integrated imaging and tracking algorithm that synthesizes back-projection (BP) and track-before-detect (TBD) techniques has been derived for efficient bidirectional information exchange. BP imaging provides high-resolution measurements to refine tracking parameters, while TBD tracking offers predictive data to guide the adaptive partitioning of the imaging area. Additionally, we enhance the traditional dynamic programming-based TBD (DP-TBD) algorithm by incorporating the morphological features of target shadows, allowing for more accurate corrections and refinements of predicted states. This enhancement significantly improves both tracking accuracy and speed. The experimental results from airborne radar data have proven the capability of the proposed algorithm to achieve both efficient PAFR imaging and fast target tracking simultaneously, which paves the way for more potential applications in ViSAR.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"576-590"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792926","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 : 2025-03-19DOI: 10.1109/TRS.2025.3571284
Stefan Hägele;Fabian Seguel;Sabri Mustafa Kahya;Eckehard Steinbach
The ability of millimeter-wave (mmWave) radar to penetrate lightweight materials and provide nonvisual insights into obscured areas represents a significant advantage over camera or LiDAR sensors. This capability enables mmWave radar to detect humans behind thin walls or identify occluded objects stored within luggage or packages. The latter capability is particularly valuable in industrial, logistics, and manufacturing applications, where the ability to “look inside the box without opening it” can greatly enhance the efficiency and security. However, the current state of the art in these applications relies on expensive custom-built large antenna array imaging scanners, coupled with image-based object detection algorithms, to detect and classify occluded or concealed objects. To address this challenge more efficiently, we propose a lightweight classification approach for detecting various occluded objects inside a cardboard box. We employ a standard off-the-shelf mmWave 4-D frequency-modulated continuous wave (FMCW) imaging radar. This is combined with a deep learning-based classification method in the form of a dual-stream convolutional neural network (CNN) approach to process complex in-phase and quadrature (IQ) radar signals. This approach reaches in our experiments an overall accuracy of 95.15% on average over a collection of ten different concealed objects.
{"title":"Occluded Object Classification With mmWave MIMO Radar IQ Signals Using Dual-Stream Convolutional Neural Networks","authors":"Stefan Hägele;Fabian Seguel;Sabri Mustafa Kahya;Eckehard Steinbach","doi":"10.1109/TRS.2025.3571284","DOIUrl":"https://doi.org/10.1109/TRS.2025.3571284","url":null,"abstract":"The ability of millimeter-wave (mmWave) radar to penetrate lightweight materials and provide nonvisual insights into obscured areas represents a significant advantage over camera or LiDAR sensors. This capability enables mmWave radar to detect humans behind thin walls or identify occluded objects stored within luggage or packages. The latter capability is particularly valuable in industrial, logistics, and manufacturing applications, where the ability to “look inside the box without opening it” can greatly enhance the efficiency and security. However, the current state of the art in these applications relies on expensive custom-built large antenna array imaging scanners, coupled with image-based object detection algorithms, to detect and classify occluded or concealed objects. To address this challenge more efficiently, we propose a lightweight classification approach for detecting various occluded objects inside a cardboard box. We employ a standard off-the-shelf mmWave 4-D frequency-modulated continuous wave (FMCW) imaging radar. This is combined with a deep learning-based classification method in the form of a dual-stream convolutional neural network (CNN) approach to process complex in-phase and quadrature (IQ) radar signals. This approach reaches in our experiments an overall accuracy of 95.15% on average over a collection of ten different concealed objects.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"789-798"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272917","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 : 2025-03-16DOI: 10.1109/TRS.2025.3570977
David K. Richardson;T. Patrick Xiao;R. Derek West;Christopher H. Bennett;Sapan Agarwal
As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. In this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.
{"title":"Analog In-Memory Computing for the Synthetic Aperture Radar Polar Format Algorithm","authors":"David K. Richardson;T. Patrick Xiao;R. Derek West;Christopher H. Bennett;Sapan Agarwal","doi":"10.1109/TRS.2025.3570977","DOIUrl":"https://doi.org/10.1109/TRS.2025.3570977","url":null,"abstract":"As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. In this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"811-817"},"PeriodicalIF":0.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308577","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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