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coarray tensor Train Aided Target Localization for Bistatic MIMO Radar

IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 46-50页

作者： Xie, Qianpeng Wen, Fangqing Xie, Xiaoxia Wang, Zhanling Pan, Xiaoyi Natl Univ Def Technol State Key Lab Complex Electromagnet Environm Effec Changsha 410073 Peoples R China Hubei Univ Automot Technol Inst Vehicle Informat Control & Network Technol Shiyan 442002 Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Peoples R China

In this letter, a coarray tensor Train (TT) decomposition method is proposed to locate the targets in bistatic Multiple-Input Multiple-Output (MIMO) radar with uniform planar array (UPA) geometry. Initially, a five-dimensional (5-D) tensor model is established to preserve the multi-dimensional structure in the receive part. Subsequently, a four-dimensional (4-D) low-rank tensor can be obtained by removing redundant elements in the difference coarray of the eight-dimensional (8-D) covariance tensor. The TT-SVD algorithm is then applied to convert this 4-D tensor into a sequential product of lower-order TT-cores. Compared to CANDECOMP/PARAFAC decomposition (CPD) and Tucker decomposition (TD), the advantages of TTD include flexible multi-way data representation and mitigation of the curse of dimensionality. Furthermore, by using the relationship between Vandermonde factors matrices and TT-cores, a new method by combining different TT-cores is devised to estimate the angle parameters. Simulation results confirm the superiority of the proposed TT-aided method over other tensor-based methods for bistatic MIMO radar.

关键词： tensors MIMO radar Transmission line matrix methods Matrix decomposition Vectors Planar arrays Estimation Covariance matrices Location awareness Geometry Bistatic MIMO radar coarray tensor angle parameter estimation tensor train decomposition (TTD) TT-SVD algorithm

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Parameter Estimation With Bistatic MIMO Radar: A coarray tensor Decomposition Framework

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4450-4465页

作者： Wang, Wenshuai Wang, Xianpeng Guo, Yuehao Gui, Guan Hainan Univ State Key Lab Marine Resource Utilizat South China Haikou 570228 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China

The traditional parameter estimation method based on the matrix framework in multiple-input-multiple-output (MIMO) radar with sparse arrays loses the information of the tensor signal structure, resulting in performance degradation. Therefore, this article primarily investigates parameter estimation methods for bistatic MIMO radar based on a tensor decomposition framework. The conventional tensor based on self-correlation discards two virtual array elements, leading to a loss of performance and degrees of freedom (DOFs). To address this issue, this article proposes a coarray tensor decomposition framework for direction of departure (DOD) and direction of arrival (DOA) estimation, introducing an approach for constructing a coarray tensor. First, a virtual difference coarray is constructed using two subtensors. Then, a coarray tensor is constructed based on the non-Hermitian structure of the cross-correlation signal matrix. Next, the resulting coarray tensor is reconstructed to achieve optimal source identifiability. Nevertheless, the increase in dimensionality resulting from the reconstructed coarray tensor leads to higher algorithmic complexity. To mitigate this, we perform a real-valued transformation on the reconstructed coarray tensor, which speeds up the execution of the method. Additionally, the proposed method also features the capability to suppress colored noise. Theoretical analysis indicates that the reconstructed coarray tensor has more DOFs than the original coarray tensor. Simulation results show that the proposed method has larger DOFs and good parameter estimation performance.

关键词： tensors MIMO radar Estimation Radar Direction-of-arrival estimation Colored noise Complexity theory Aerospace and electronic systems Radar antennas Accuracy coarray tensor multiple-input multiple-output (MIMO) radar parameter estimation

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coarray tensor Completion for DOA Estimation

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 5472-5486页

作者： Zheng, Hang Shi, Zhiguo Zhou, Chengwei de Almeida, Andre L. F. Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China Key Lab Collaborat Sensing & Autonomous Unmanned Hangzhou 310015 Peoples R China Univ Fed Ceara Dept Teleinformat Engn BR-60020181 Fortaleza Brazil

Sparse array direction-of-arrival (DOA) estimation using tensor model has been developed to handle multidimensional sub-Nyquist sampled signals. Furthermore, augmented virtual arrays can be derived for Nyquist-matched coarray tensor processing. However, the partially augmentable sparse array corresponds to a discontinuous virtual array, whereas the existing methods can only utilize its continuous part. Conventional virtual linear array interpolation techniques complete coarray covariance matrices with dispersed missing elements, but fail to complete the coarray tensor with whole missing slices. In this article, we propose a coarray tensor completion algorithm for 2-D DOA estimation, where the coarray tensor statistics can be entirely exploited. In particular, in order to impose an effective low-rank regularization on the slice-missing coarray tensor, we propose shift dimensional augmenting and coarray tensor reshaping approaches to reformulate a structured coarray tensor with sufficiently dispersed missing elements. Furthermore, the shape of the reformulated coarray tensor is optimized by maximizing the dispersion-to-percentage ratio of missing elements. As such, a coarray tensor nuclear norm minimization problem can be designed to optimize the completed coarray tensor corresponding to a filled virtual array, based on which the closed-form DOA estimation is achieved. Meanwhile, the global convergence of the coarray tensor completion is theoretically proved. Simulation results demonstrate the effectiveness of the proposed algorithm compared with other matrix-based and tensor-based methods.

关键词： tensors Estimation Direction-of-arrival estimation Array signal processing Sparse matrices Transmission line matrix methods Sensor arrays coarray tensor direction-of-arrival (DOA) estimation sparse array tensor completion

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coarray tensor Direction-of-Arrival Estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 1128-1142页

作者： Zheng, Hang Zhou, Chengwei Shi, Zhiguo Gu, Yujie Zhang, Yimin D. Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Key Lab Collaborat Sensing & Autonomous Unmanned Hangzhou 310015 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China Aptiv Adv Safety & User Experience Agoura Hills CA 91301 USA Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA

Augmented coarrays can be derived from spatially undersampled signals of sparse arrays for underdetermined direction-of-arrival (DOA) estimation. With the extended dimension of sparse arrays, the sampled signals can be modeled as sub-Nyquist tensors, thereby enabling coarray tensor processing to enhance the estimation performance. The existing methods, however, are not applicable to generalized multi-dimensional sparse arrays, such as sparse planar array and sparse cubic array, and have not fully exploited the achievable source identifiability. In this paper, we propose a coarray tensor DOA estimation algorithm for multi-dimensional structured sparse arrays and investigate an optimal coarray tensor structure for source identifiability enhancement. Specifically, the cross-correlation tensor between sub-Nyquist tensor signals is calculated to derive a coarray tensor. Based on the uniqueness condition for coarray tensor decomposition, the achievable source identifiability is analysed. Furthermore, to enhance the source identifiability, a dimension increment approach is proposed to embed shifting information in the coarray tensor. The shifting-embedded coarray tensor is subsequently reshaped to optimize the source identifiability. The resulting maximum number of degrees-of-freedom is theoretically proved to exceed the number of physical sensors. Hence, the optimally reshaped coarray tensor can be decomposed for underdetermined DOA estimation with closed-form solutions. Simulation results demonstrate the effectiveness of the proposed algorithm in both underdetermined and overdetermined cases.

关键词： coarray tensor direction-of-arrival estimation source identifiability sparse array sub-Nyquist tensor

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coarray tensor-Based Angle Estimation for Bistatic MIMO Radar With a Dilated Moving Receive Array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 8995-9009页

作者： Luo, Shuai Wang, Yuexian Li, Jianying Tellambura, Chintha Rodrigues, Joel J. P. C. Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada Lusofona Univ COPELABS P-1749024 Lisbon Portugal

Utilizing sparse arrays is a very effective and commonly used method to enhance the degrees of freedom (DOFs) of multiple-input multiple-output (MIMO) radar. Unfortunately, as research on sparse arrays has matured, it has become difficult to greatly improve the DOFs by relying on array structure design only. Moreover, the existing angle estimation methods for sparse MIMO radar would process data under a matrix-based framework rather than the entire coarray tensor, thus suffering some loss in angle estimation performance. In this article, we extend the DOFs of MIMO radar by exploiting sparse array motion and propose an angle estimation method exploiting coarray tensor. First, we not only use sparse arrays at the transmitter and receiver parts of MIMO radar but also dilate the interelement spacing of the receive array on a moving platform. We set the transmitted signal as periodic, and further expand the DOFs and virtual aperture of MIMO radar by using the aperture synthesis technique introduced by array motion. Second, we build a self-correlation tensor model and reshape it to produce an optimal tensor with the highest DOFs that can be obtained under the uniqueness condition of parallel factor decomposition. Third, we theoretically analyze the achievable DOFs of the proposed method and show that the maximum number of detectable targets of bistatic MIMO radar can be increased to about three times. Simulation results verify the correctness of the theoretical analysis and demonstrate the superior estimation performance of our proposed method.

关键词： tensors MIMO radar Estimation Arrays Array signal processing Receivers Apertures Angle estimation array motion bistatic multiple-input multiple-output (MIMO) radar coarray tensor parallel factor decomposition

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Coupled coarray tensor CPD for DOA Estimation With Coprime L-Shaped Array

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 1545-1549页

作者： Zheng, Hang Shi, Zhiguo Zhou, Chengwei Haardt, Martin Chen, Jian Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Alibaba Zhejiang Univ Joint Res Inst Frontier Tec Hangzhou 310027 Peoples R China Ilmenau Univ Technol Commun Res Lab D-98693 Ilmenau Germany Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China

Conventional canonical polyadic decomposition (CPD) approach for tensor-based sparse array direction-of-arrival (DOA) estimation typically partitions the coarray statistics to generate a full-rank coarray tensor for decomposition. However, such an operation ignores the spatial relevance among the partitioned coarray statistics. In this letter, we propose a coupled coarray tensor CPD-based two-dimensional DOA estimation method for a specially designed coprime L-shaped array. In particular, a shifting coarray concatenation approach is developed to factorize the partitioned fourth-order coarray statistics into multiple coupled coarray tensors. To make full use of the inherent spatial relevance among these coarray tensors, a coupled coarray tensor CPD approach is proposed to jointly decompose them for high-accuracy DOA estimation in a closed-form manner. According to the uniqueness condition analysis on the coupled coarray tensor CPD, an increased number of degrees-of-freedom for the proposed method is guaranteed.

关键词： tensors Estimation Direction-of-arrival estimation Array signal processing Sensor arrays Antenna arrays Geometry coarray tensor coprime L-shaped array coupled CPD DOA estimation

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TWO-DIMENSIONAL DOA ESTIMATION FOR COPRIME PLANAR ARRAY: A coarray tensor-BASED SOLUTION

TWO-DIMENSIONAL DOA ESTIMATION FOR COPRIME PLANAR ARRAY: A C...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zheng, Hang Zhou, Chengwei Gu, Yujie Shi, Zhiguo Zhejiang Univ Fac Informat Technol Hangzhou 310027 Peoples R China Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

Coprime arrays can cope with the underdetermined case for direction-of-arrival (DOA) estimation. However, the popular matrix-based coarray signal processing approaches suffer performance loss on the underlying characteristics among the multi-dimensional signals. To address this problem, we propose a novel coarray tensor-based two-dimensional underdetermined DOA estimation method for coprime planar array in this paper, where both the multi-dimensional information of the received signals and the augmented coarray are effectively utilized. The received signal tensors of the coprime planar array are constructed by concatenating each snapshot, which are then transformed to an augmented uniform rectangular array statistics for extending the effective array aperture. Subsequently, the tensorization technique is adopted to increase the degrees-of-freedom of the proposed DOA estimation method, and a structured coarray tensor is optimized for super-resolution DOA estimation. The effectiveness of the proposed method is verified via simulation results.

关键词： coarray tensor coprime planar array DOA estimation structured tensorization underdetermined

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DOA ESTIMATION VIA coarray tensor COMPLETION WITH MISSING SLICES 47

DOA ESTIMATION VIA COARRAY TENSOR COMPLETION WITH MISSING SL...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zheng, Hang Zhou, Chengwei de Almeida, Andre L. F. Gu, Yujie Shi, Zhiguo Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Univ Fed Ceara Dept Teleinformat Engn BR-60020181 Fortaleza Ceara Brazil Aptiv Elect & Safety Agoura Hills CA 91301 USA Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China

ISBN: (纸本)9781665405409

In this paper, a coarray tensor completion-based direction-of-arrival (DOA) estimation method is proposed for coprime planar array. To perform Nyquist-matched coarray signal processing, the completion of the coarray tensor corresponding to an augmented discontinuous virtual array is pursued. However, it is difficult to impose a low-rank regularization on the incomplete coarray tensor with slices of missing elements for its completion. To solve this problem, a structural tensorization approach is designed to reshape the incomplete coarray tensor into one with distributed missing elements. As such, a coarray tensor completion problem based on tensor nuclear norm minimization is formulated to complete these missing elements. By exploiting the filled virtual array obtained from the completed coarray tensor, a super-resolution DOA estimation can be achieved in closed-form.

关键词： coarray tensor coprime planar array direction-of-arrival estimation tensor completion

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Decomposed CNN for Sub-Nyquist tensor-Based 2-D DOA Estimation

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IEEE SIGNAL PROCESSING LETTERS 2023年 30卷 708-712页

作者： Zheng, Hang Zhou, Chengwei Voroboyv, Sergiy A. Wang, Qing Shi, Zhiguo Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Aalto Univ Dept Signal Proc & Acoust Espoo 02150 Finland Key Lab Collaborat Sensing & Autonomous Unmanned S Hangzhou 310015 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China

Direction-of-arrival (DOA) estimation using sub-Nyquist tensor signals benefits from enhanced performance by extracting structural angular information with multi-dimensional sparse arrays. Although convolutional neural network (CNN) has been employed to achieve efficient DOA estimation in challenging conditions, conventional methods demand excessive memory storage and computation power to process sub-Nyquist tensor statistics. In this letter, we propose a decomposed CNN for sub-Nyquist tensor-based 2-D DOA estimation, where an augmented coarray tensor is derived and used as the network input. To compress convolution kernels for efficient coarray tensor propagation, we develop a convolution kernel decomposition approach. This enables the acquisition of canonical polyadic (CP) factors containing compressed parameters. Performing decomposable convolution between the coarray tensor and the CP factors leads to resource-efficient DOA estimation. Our simulation results indicate that the proposed method conserves system resources while maintaining competitive performance.

关键词： coarray tensor convolution kernel decomposition DOA estimation sub-Nyquist tensor

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2-D DOA Estimation for Coprime Cubic Array: A Cross-correlation tensor Perspective

2-D DOA Estimation for Coprime Cubic Array: A Cross-correlat...

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International Symposium on Antennas and Propagation (ISAP)

作者： Zheng, Hang Zhou, Chengwei Wang, Yong Shi, Zhiguo Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Alibaba Zhejiang Univ Joint Inst Frontier Technol Hangzhou Peoples R China

ISBN: (纸本)9784885523267

In this paper, a cross-correlation coarray tensor-based 2-D direction-of-arrival (DOA) estimation method for coprime cubic array (CCA) is proposed. Since the CCA received signals characterized by two coprime tensors cannot be collectively shaped as a single tensor to operate auto-correlation, the cross-correlation is introduced to calculate the coarray tensor statistics of CCA. A Nyquist-matched 4-D coarray tensor is then obtained for 2-D DOA estimation, where the designed structured tensorization is applied without spatial smoothing. Simulation results verify the effectiveness of the proposed method.

关键词： DOA estimation coprime cubic array cross-correlation coarray tensor structured tensorization

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