This article describes a method of deriving and verifying hardware specification of a direct digital frequency synthesizer (DDFS) and radio frequency digital-to-analog converter (RFDAC)-based frequency-modulated continuous-wave (FMCW) modulator. The analysis of the concept is conducted by studying the digital nonlinearities, such as amplitude quantization noise, phase quantization noise, and frequency error in the ramp, and analog nonlinearities, such as IQ quadrature error and counter inter modulation-3 (CIM3) of the RFDAC. The impact of the nonlinearities on the detectability of target in the intermediate frequency (IF) spectrum is evaluated with the MATLAB model of the frequency modulator. The outcome of the concept evaluation predicts the low-level hardware specifications needed for the design such as amplitude quantization, phase quantization, expected noise level, spur positions in the target IF spectrum, and frequency error in the ramp. The RFDAC-based FMCW modulator is manufactured in 28-nm technology with the derived parameters and the time-domain data of a frequency ramp from 5 to 9-GHz in 100 $mu $ s is sampled during measurement. The data are postprocessed to confirm the predictions made by the simulation model and to characterize ramp linearity, dynamic phase noise (DPN), and settling time of the ramp. The frequency error for a 4-GHz ramp in 100- $mu $ s duration is ±100kHz, and the settling time in the postprocessed result is in the 20-ns range.
{"title":"Concept Evaluation of a DDFS and RFDAC-Based FMCW Modulator","authors":"Soumya Krishnapuram Sireesh;Niels Christoffers;Christoph Wagner;Andreas Stelzer","doi":"10.1109/TRS.2024.3410137","DOIUrl":"https://doi.org/10.1109/TRS.2024.3410137","url":null,"abstract":"This article describes a method of deriving and verifying hardware specification of a direct digital frequency synthesizer (DDFS) and radio frequency digital-to-analog converter (RFDAC)-based frequency-modulated continuous-wave (FMCW) modulator. The analysis of the concept is conducted by studying the digital nonlinearities, such as amplitude quantization noise, phase quantization noise, and frequency error in the ramp, and analog nonlinearities, such as IQ quadrature error and counter inter modulation-3 (CIM3) of the RFDAC. The impact of the nonlinearities on the detectability of target in the intermediate frequency (IF) spectrum is evaluated with the MATLAB model of the frequency modulator. The outcome of the concept evaluation predicts the low-level hardware specifications needed for the design such as amplitude quantization, phase quantization, expected noise level, spur positions in the target IF spectrum, and frequency error in the ramp. The RFDAC-based FMCW modulator is manufactured in 28-nm technology with the derived parameters and the time-domain data of a frequency ramp from 5 to 9-GHz in 100\u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000 s is sampled during measurement. The data are postprocessed to confirm the predictions made by the simulation model and to characterize ramp linearity, dynamic phase noise (DPN), and settling time of the ramp. The frequency error for a 4-GHz ramp in 100-\u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000 s duration is ±100kHz, and the settling time in the postprocessed result is in the 20-ns range.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"618-631"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539221","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 : 2024-06-03DOI: 10.1109/TRS.2024.3409029
Matthew G. Gaydos;David J. Love;Taejoon Kim
Multiple-input-multiple-output (MIMO) radar systems have become a heavily researched topic in recent years due to the improved diversity techniques when compared to that of 1-D radar waveforms. Although there exist a myriad of techniques to design the transmit power distribution for MIMO radar systems, stringent constant modulus power limitations imposed by modern high-power amplifiers (HPAs) complicate their practical implementation. Ideally, a technique that emulates the spatial filtering flexibility of precoded MIMO communications is desired. To achieve such flexibility, an MIMO radar framework must be introduced that guarantees constant modulus for all combinations of signal sets and precoders, allowing simple interchangeability between components of the waveform—whether that be a new transmit power distribution or an adjusted dynamic MIMO radar waveform. In this article, we show that designing a constant modulus precoded MIMO radar can be achieved in a practical manner, utilizing alphabet-based waveform construction. This technique leverages the use of two sets for which the product of any pair of vectors, one from each alphabet, guarantees a fixed constant. By utilizing these sets as alphabets to design the waveform, it is possible to implement MIMO radar waveforms of any rank and enable the decoupling of the precoder and MIMO radar waveform design. This article presents a framework that achieves the aforementioned requirements by utilizing the properties of Zadoff-Chu (ZC) sequences and the discrete Fourier transform (DFT) matrix. By restricting construction of the precoder and signal set to be in part formed from finite alphabets, it is shown that the constant modulus constraint is achieved for any precoder and any signal set combination.
与一维雷达波形相比，多输入多输出（MIMO）雷达系统改进了分集技术，因此成为近年来研究的热点。虽然有无数种技术可用于设计 MIMO 雷达系统的发射功率分布，但现代大功率放大器（HPA）施加的严格恒定模数功率限制使其实际应用变得复杂。理想情况下，我们需要一种技术来模拟预编码多输入多输出通信的空间滤波灵活性。要实现这种灵活性，必须引入一种 MIMO 雷达框架，它能保证信号集和预编码器的所有组合都具有恒定的模数，并允许波形各组成部分之间的简单互换--无论是新的发射功率分布还是调整后的动态 MIMO 雷达波形。在本文中，我们将展示如何利用基于字母的波形构造，以实用的方式设计恒定模预编码 MIMO 雷达。这种技术利用了两个集合，其中任何一对矢量的乘积（来自每个字母表的一个矢量）都能保证一个固定常数。利用这些集合作为字母表来设计波形，就有可能实现任何等级的 MIMO 雷达波形，并实现前置编码器和 MIMO 雷达波形设计的解耦。本文提出了一个框架，利用扎多夫-楚（ZC）序列和离散傅立叶变换（DFT）矩阵的特性来实现上述要求。通过限制前置编码器和信号集的构造部分由有限字母组成，证明了恒定模数约束可在任何前置编码器和任何信号集组合中实现。
{"title":"Constant Modulus Precoded MIMO Radar Based on Zadoff-Chu Sequences","authors":"Matthew G. Gaydos;David J. Love;Taejoon Kim","doi":"10.1109/TRS.2024.3409029","DOIUrl":"https://doi.org/10.1109/TRS.2024.3409029","url":null,"abstract":"Multiple-input-multiple-output (MIMO) radar systems have become a heavily researched topic in recent years due to the improved diversity techniques when compared to that of 1-D radar waveforms. Although there exist a myriad of techniques to design the transmit power distribution for MIMO radar systems, stringent constant modulus power limitations imposed by modern high-power amplifiers (HPAs) complicate their practical implementation. Ideally, a technique that emulates the spatial filtering flexibility of precoded MIMO communications is desired. To achieve such flexibility, an MIMO radar framework must be introduced that guarantees constant modulus for all combinations of signal sets and precoders, allowing simple interchangeability between components of the waveform—whether that be a new transmit power distribution or an adjusted dynamic MIMO radar waveform. In this article, we show that designing a constant modulus precoded MIMO radar can be achieved in a practical manner, utilizing alphabet-based waveform construction. This technique leverages the use of two sets for which the product of any pair of vectors, one from each alphabet, guarantees a fixed constant. By utilizing these sets as alphabets to design the waveform, it is possible to implement MIMO radar waveforms of any rank and enable the decoupling of the precoder and MIMO radar waveform design. This article presents a framework that achieves the aforementioned requirements by utilizing the properties of Zadoff-Chu (ZC) sequences and the discrete Fourier transform (DFT) matrix. By restricting construction of the precoder and signal set to be in part formed from finite alphabets, it is shown that the constant modulus constraint is achieved for any precoder and any signal set combination.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"677-689"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602504","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}
This article presents a comprehensive method for radar-camera calibration with a primary focus on real-time projection, addressing the critical need for precise spatial and temporal alignment between radar and camera sensor modalities. The research introduces a novel methodology for calibration utilizing geometrical transformation, incorporating radar corner reflectors to establish correspondences. This methodology applies to post-automotive manufacturing for integration into radar-camera applications such as advanced driver-assistance systems (ADASs), adaptive cruise control (ACC), collision warning, and mitigation systems. It also serves post-production for sensor installation and algorithm development. The proposed approach employs an advanced algorithm to optimize spatial and temporal synchronization and radar and camera data alignment, ensuring accuracy in multimodal sensor fusion. Rigorous validation through extensive testing demonstrates the efficiency and reliability of the proposed system. The results show that the calibration method is highly accurate compared to the existing state-of-the-art methods, with minimal errors, an average Euclidean distance (AED) of 1.447, and a root-mean-square reprojection error (RMSRE) of (0.1720, 0.5965), indicating a highly efficient spatial synchronization method. During real-time projection, the proposed algorithm for temporal synchronization achieves an average latency of 35 ms between frames.
{"title":"An Efficient Approach for Calibration of Automotive Radar–Camera With Real-Time Projection of Multimodal Data","authors":"Nitish Kumar;Ayush Dasgupta;Venkata Satyanand Mutnuri;Rajalakshmi Pachamuthu","doi":"10.1109/TRS.2024.3408231","DOIUrl":"https://doi.org/10.1109/TRS.2024.3408231","url":null,"abstract":"This article presents a comprehensive method for radar-camera calibration with a primary focus on real-time projection, addressing the critical need for precise spatial and temporal alignment between radar and camera sensor modalities. The research introduces a novel methodology for calibration utilizing geometrical transformation, incorporating radar corner reflectors to establish correspondences. This methodology applies to post-automotive manufacturing for integration into radar-camera applications such as advanced driver-assistance systems (ADASs), adaptive cruise control (ACC), collision warning, and mitigation systems. It also serves post-production for sensor installation and algorithm development. The proposed approach employs an advanced algorithm to optimize spatial and temporal synchronization and radar and camera data alignment, ensuring accuracy in multimodal sensor fusion. Rigorous validation through extensive testing demonstrates the efficiency and reliability of the proposed system. The results show that the calibration method is highly accurate compared to the existing state-of-the-art methods, with minimal errors, an average Euclidean distance (AED) of 1.447, and a root-mean-square reprojection error (RMSRE) of (0.1720, 0.5965), indicating a highly efficient spatial synchronization method. During real-time projection, the proposed algorithm for temporal synchronization achieves an average latency of 35 ms between frames.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"573-582"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315167","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 : 2024-04-29DOI: 10.1109/TRS.2024.3394896
Augusto Aubry;Antonio De Maio;Luca Pallotta
The modern battlefield scenario is strongly influenced by the innovative capabilities of multifunction phased array radars (MPARs), which can perform a plethora of sensing and communication (COM) activities sequentially or in parallel. In fact, the MPAR can functionally cluster its phased array into bespoke subapertures implementing different tasks. Accordingly, a portion of the other available resources, e.g., bandwidth, power-aperture product (PAP), and time, is also assigned to each subaperture, and the grand challenge is the definition of strategies for optimal scheduling of the tasks to be executed. In this respect, a rule-based algorithm for task scheduling is proposed in this article. In a nutshell, in each time window, the procedure first allocates the radar tasks (viz., volume search, cued search, update, and confirmation tracking) and then utilizes the COM looks to fill the empty intraslot time left by the radar tasks. When there are two concurrent looks, the allocation is performed according to their priorities. Moreover, if the bandwidth and PAP are sufficient, some of them can be also scheduled in parallel. Interesting results in terms of bandwidth and time occupancy efficiency are observed from simulations conducted in challenging scenarios comprising also multiple maneuvering targets.
多功能相控阵雷达（MPAR）的创新能力对现代战场场景产生了重大影响，它可以连续或并行地执行大量传感和通信（COM）活动。事实上，MPAR 可以在功能上将其相控阵集群为执行不同任务的定制子孔径。因此，其他可用资源（如带宽、功率-孔径乘积（PAP）和时间）的一部分也被分配给每个子孔径，而最大的挑战在于如何定义待执行任务的优化调度策略。为此，本文提出了一种基于规则的任务调度算法。简而言之，在每个时间窗口中，该程序首先分配雷达任务（即体积搜索、提示搜索、更新和确认跟踪），然后利用 COM 观测来填补雷达任务留下的空隙内时间。当有两个并发观测任务时，将根据它们的优先级进行分配。此外，如果带宽和 PAP 足够，其中一些任务也可以并行调度。通过在具有挑战性的场景（包括多个机动目标）中进行模拟，在带宽和时间占用效率方面观察到了有趣的结果。
{"title":"A Priority-Based Scheduling Scheme for Search, Track, and Communications in MPARs","authors":"Augusto Aubry;Antonio De Maio;Luca Pallotta","doi":"10.1109/TRS.2024.3394896","DOIUrl":"https://doi.org/10.1109/TRS.2024.3394896","url":null,"abstract":"The modern battlefield scenario is strongly influenced by the innovative capabilities of multifunction phased array radars (MPARs), which can perform a plethora of sensing and communication (COM) activities sequentially or in parallel. In fact, the MPAR can functionally cluster its phased array into bespoke subapertures implementing different tasks. Accordingly, a portion of the other available resources, e.g., bandwidth, power-aperture product (PAP), and time, is also assigned to each subaperture, and the grand challenge is the definition of strategies for optimal scheduling of the tasks to be executed. In this respect, a rule-based algorithm for task scheduling is proposed in this article. In a nutshell, in each time window, the procedure first allocates the radar tasks (viz., volume search, cued search, update, and confirmation tracking) and then utilizes the COM looks to fill the empty intraslot time left by the radar tasks. When there are two concurrent looks, the allocation is performed according to their priorities. Moreover, if the bandwidth and PAP are sufficient, some of them can be also scheduled in parallel. Interesting results in terms of bandwidth and time occupancy efficiency are observed from simulations conducted in challenging scenarios comprising also multiple maneuvering targets.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"471-481"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10510313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905303","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 : 2024-04-22DOI: 10.1109/TRS.2024.3392439
Paulo Ricardo Marques de Araujo;Aboelmagd Noureldin;Sidney Givigi
This paper presents a deep learning framework for the estimation of land vehicle ego-velocity using Frequency Modulated Continuous Wave (FMCW) automotive radars, addressing the challenges of data sparsity and noise without the need for extrinsic radar calibration. By structuring radar scans into image-based and voxel-based networks, our approach demonstrates robust ego-velocity estimation across multiple sensor configurations and orientations. Experimental results from three distinct datasets—RadarScenes, NavINST, and MSC-RAD4R—validate the framework’s effectiveness, showing superior performance over traditional methods. The models’ adaptability to various sensor specifications and their computational efficiency highlight their potential for real-time applications. We made our implementation open-source at: https://github.com/paaraujo/deep-ego-velocity .
{"title":"Toward Land Vehicle Ego-Velocity Estimation Using Deep Learning and Automotive Radars","authors":"Paulo Ricardo Marques de Araujo;Aboelmagd Noureldin;Sidney Givigi","doi":"10.1109/TRS.2024.3392439","DOIUrl":"https://doi.org/10.1109/TRS.2024.3392439","url":null,"abstract":"This paper presents a deep learning framework for the estimation of land vehicle ego-velocity using Frequency Modulated Continuous Wave (FMCW) automotive radars, addressing the challenges of data sparsity and noise without the need for extrinsic radar calibration. By structuring radar scans into image-based and voxel-based networks, our approach demonstrates robust ego-velocity estimation across multiple sensor configurations and orientations. Experimental results from three distinct datasets—RadarScenes, NavINST, and MSC-RAD4R—validate the framework’s effectiveness, showing superior performance over traditional methods. The models’ adaptability to various sensor specifications and their computational efficiency highlight their potential for real-time applications. We made our implementation open-source at: \u0000<uri>https://github.com/paaraujo/deep-ego-velocity</uri>\u0000.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"460-470"},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820236","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 : 2024-04-22DOI: 10.1109/TRS.2024.3392073
Francesco Mancuso;Elisa Giusti;Brian Ng;Marco Martorella
The Three-Dimensional Interferometric Inverse Synthetic Aperture Radar Imaging (3D InISAR) method tackles the interpretability challenges associated with two-dimensional ISAR. It achieves this by providing a 3D representation of the target, offering a more comprehensive understanding of its shape and features. However, this approach faces challenges related to interferometric measurement ambiguity, especially in operational scenarios where factors such as target type and range of the target come into play. Conventional methods for interferogram unwrapping used in Interferometric SAR systems for topographic mapping cannot be directly applied to man-made objects in ISAR due to the discrete nature of ISAR imaging, which violates the assumption of spatial continuity. To address these issues, various multi-receiver solutions have been proposed in the literature. This paper introduces a different approach: a maximum likelihood-based dual-frequency technique applied to 3D InISAR imaging. Leveraging the frequency diversity inherent in a wideband receiver and utilizing two non-overlapping sub-bandwidths, this method effectively resolves measurement ambiguity. Testing the method in a simulated scenarios highlights the enhanced reconstruction abilities of the method and the benefits of utilizing extended physical baselines.
{"title":"Dual-Frequency Phase Unwrapping for 3D InISAR Imaging of Non-Cooperative Targets","authors":"Francesco Mancuso;Elisa Giusti;Brian Ng;Marco Martorella","doi":"10.1109/TRS.2024.3392073","DOIUrl":"https://doi.org/10.1109/TRS.2024.3392073","url":null,"abstract":"The Three-Dimensional Interferometric Inverse Synthetic Aperture Radar Imaging (3D InISAR) method tackles the interpretability challenges associated with two-dimensional ISAR. It achieves this by providing a 3D representation of the target, offering a more comprehensive understanding of its shape and features. However, this approach faces challenges related to interferometric measurement ambiguity, especially in operational scenarios where factors such as target type and range of the target come into play. Conventional methods for interferogram unwrapping used in Interferometric SAR systems for topographic mapping cannot be directly applied to man-made objects in ISAR due to the discrete nature of ISAR imaging, which violates the assumption of spatial continuity. To address these issues, various multi-receiver solutions have been proposed in the literature. This paper introduces a different approach: a maximum likelihood-based dual-frequency technique applied to 3D InISAR imaging. Leveraging the frequency diversity inherent in a wideband receiver and utilizing two non-overlapping sub-bandwidths, this method effectively resolves measurement ambiguity. Testing the method in a simulated scenarios highlights the enhanced reconstruction abilities of the method and the benefits of utilizing extended physical baselines.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"434-445"},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10506548","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140817088","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 : 2024-04-12DOI: 10.1109/TRS.2024.3388212
Lan Lan;Massimo Rosamilia;Augusto Aubry;Antonio De Maio;Guisheng Liao
This paper investigates the joint optimization of transmit parameters and receive filter in a Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar system with a uniform frequency increment from sensor to sensor. The problem is formulated as the maximization of the Signal-to-Interference-plus-Noise Ratio (SINR) at the output of the receive filter in a signal-dependent clutter environment, taking into account some practical constraints on the probing waveform and frequency increment. To tackle the resulting non-convex and NP-hard optimization problem, a Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is developed, which iteratively updates the variables block that yields the maximum increase of the objective function while keeping the others fixed. The convergence properties of the proposed algorithm are rigorously studied, and the computational complexity is analyzed. Numerical results demonstrate the effectiveness of the designed procedure under several clutter scenarios of practical relevance, including proper comparisons with counterparts.
{"title":"FDA-MIMO Transceiver Design Under the Uniform Frequency Increment Constraint","authors":"Lan Lan;Massimo Rosamilia;Augusto Aubry;Antonio De Maio;Guisheng Liao","doi":"10.1109/TRS.2024.3388212","DOIUrl":"https://doi.org/10.1109/TRS.2024.3388212","url":null,"abstract":"This paper investigates the joint optimization of transmit parameters and receive filter in a Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar system with a uniform frequency increment from sensor to sensor. The problem is formulated as the maximization of the Signal-to-Interference-plus-Noise Ratio (SINR) at the output of the receive filter in a signal-dependent clutter environment, taking into account some practical constraints on the probing waveform and frequency increment. To tackle the resulting non-convex and NP-hard optimization problem, a Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is developed, which iteratively updates the variables block that yields the maximum increase of the objective function while keeping the others fixed. The convergence properties of the proposed algorithm are rigorously studied, and the computational complexity is analyzed. Numerical results demonstrate the effectiveness of the designed procedure under several clutter scenarios of practical relevance, including proper comparisons with counterparts.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"446-459"},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10497910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140817087","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 : 2024-04-12DOI: 10.1109/TRS.2024.3387950
Fatemeh Modares Sabzevari;Robert S. C. Winter;Karumudi Rambabu
Microwave imaging has been developed recently and is used in many applications. Kirchhoff’s migration technique is one of the most popular methods to recover the geometric info of any inaccessible target from raw data. The conventional Kirchhoff’s migration assumes a uniform propagation velocity in the far-field region of the transceiver, i.e., the antenna. In the near-field region of the antenna, the pulse propagation happens at a significantly greater speed compared with the far-field region. The propagation speed of the pulse depends on the antenna dimensions and varies as a function of the distance and angle of the antenna in a nonlinear manner. This nonlinearity causes nonfocused images. In this work, a modified Kirchhoff’s migration method is proposed to take into account the nonuniformity of the propagation speed. Then, the proposed method is verified through several simulations and experiments and it is shown that the modified Kirchhoff’s migration results in focused images in the near-field region.
{"title":"A Modified Kirchhoff Migration for Microwave Imaging in Superluminal Propagation Region","authors":"Fatemeh Modares Sabzevari;Robert S. C. Winter;Karumudi Rambabu","doi":"10.1109/TRS.2024.3387950","DOIUrl":"https://doi.org/10.1109/TRS.2024.3387950","url":null,"abstract":"Microwave imaging has been developed recently and is used in many applications. Kirchhoff’s migration technique is one of the most popular methods to recover the geometric info of any inaccessible target from raw data. The conventional Kirchhoff’s migration assumes a uniform propagation velocity in the far-field region of the transceiver, i.e., the antenna. In the near-field region of the antenna, the pulse propagation happens at a significantly greater speed compared with the far-field region. The propagation speed of the pulse depends on the antenna dimensions and varies as a function of the distance and angle of the antenna in a nonlinear manner. This nonlinearity causes nonfocused images. In this work, a modified Kirchhoff’s migration method is proposed to take into account the nonuniformity of the propagation speed. Then, the proposed method is verified through several simulations and experiments and it is shown that the modified Kirchhoff’s migration results in focused images in the near-field region.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"498-503"},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078793","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}
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}
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