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DOA estimation based on sum-difference coarray with virtual array interpolation concept

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2021年第1期2021卷 1-13页

作者： Ding, Yarong Ren, Shiwei Wang, Weijiang Xue, Chengbo Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China

The sum-difference coarray is the union of difference coarray and the sum coarray, which is capable to obtain a higher number of degrees of freedom (DOF) than the difference coarray. However, this method fails to use all information provided by the coprime array because of the existence of holes. In this paper, we introduce the virtual array interpolation into the sum-difference coarray domain. After interpolating the virtual array, we estimate the DOA by reconstructing the covariance matrix to resolve an atomic norm minimization problem in a gridless way. The proposed method is gridless and can effectively utilize the DOF of a larger virtual array. Numerical simulation results verify the effectiveness and the superior performance of the proposed algorithm.

关键词： sum-difference coarray Virtual array interpolation Atomic norm Gridless Toeplitz matrix reconstruction

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Sparsity-Based Two-Dimensional DOA Estimation for Coprime Array: From sum-difference coarray Viewpoint

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2017年第21期65卷 5591-5604页

作者： Shi, Junpeng Hu, Guoping Zhang, Xiaofei Sun, Fenggang Zhou, Hao Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China Nanjing Univ Aeronaut & Astronaut Dept Elect Engn Nanjing 210016 Jiangsu Peoples R China Shandong Agr Univ Coll Informat Sci & Engn Tai An 271018 Shandong Peoples R China

This paper addresses the issue of two-dimensional (2-D) direction of arrival (DOA) estimation with coprime planar arrays (CPPAs) via sparse representation. Our work differs from the partial spectral search approach [ 25], which suppresses the phase ambiguity by searching the common peaks of two subarrays. We focus on the coprime property of CPPA, where the sparse array extension model with sum-difference coarray (SDCA) is derived for larger degrees of freedom (DOFs). Besides, to optimize the selection of regularization parameter, we also construct a new sparse representation algorithm by estimating the errors between the signal and noise parts. Further, an iterative scheme is presented to transform the 2-D grids searching to several times of 1-D searching, where the initial values are obtained by extracting one difference coarray from SDCA. So the proposed method can achieve aperture extension, high estimation performance, and low computational complexity. Besides, the sparse array extension model for multiple-input multiple-output radars is discussed and the Cram 'er-Rao bound for 2-D DOA estimation with CPPA is also derived in detail. Finally, simulation results demonstrate the effectiveness of proposed method compared to the state-of-the-art methods.

关键词： Two-dimensional direction of arrival estimation coprime planar arrays sparse representation sum-difference coarray degrees of freedom

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Improved Nested Arrays With sum-difference coarray for DOA Estimation

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IEEE SENSORS JOURNAL 2019年第16期19卷 6986-6997页

作者： Si, Weijian Peng, Zhanli Hou, Changbo Zeng, Fuhong Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China

In this paper, we propose two kinds of improved nested arrays for direction of arrival (DOA) estimation, which are obtained by rearranging physical sensors of the prototype nested array. Specifically, the sum-difference coarrays (SDCAs) of the proposed arrays are constructed by jointly using the temporal and spatial information of the received signal. By systematically analyzing the structures of the proposed two kinds of arrays, we derive the closed-form expressions about their physical sensor positions, SDCAs, consecutive degrees of freedom (DOFs), and maximum physical array apertures, respectively. Compared to the prototype nested array which only considers the difference coarray (DCA), the proposed arrays with SDCAs have a lot more consecutive DOFs. Moreover, all physical sensors of the proposed arrays, except the one at zero point, can he placed at the positions opposite to their original ones, which will not result in a change in the consecutive DOFs. This phenomenon means that the proposed arrays have flexible array configurations with different physical array apertures, which make them more beneficial for practical application. In the end, simulation results demonstrate the superiorities of the proposed arrays in terms of detection performance and estimation accuracy.

关键词： Improved nested arrays DOA estimation sum-difference coarray degrees of freedom physical array aperture array configurations

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Zero-Redundancy Sparse Array Configuration Design Based on sum-difference coarray

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 436-440页

作者： Zhao, Pinjiao Wu, Qisong Wang, Liwei Wan, Liangtian Hu, Guobing Jinling Inst Technol Sch Elect & Informat Engn Nanjing 211169 Peoples R China Southeast Univ Key Lab Underwater Acoust Signal Proc Minist Educ Nanjing 210096 Peoples R China Nanjing Elect Devices Inst Nanjing 210007 Peoples R China Dalian Univ Technol Sch Software Dalian 116620 Peoples R China

In this letter, we present a novel strategy for designing zero-redundancy arrays exploiting the concept of the sum-difference coarray. This approach aims to eliminate redundant lags between the difference and sum coarrays, resulting in a more efficient array design. Consequently, we introduce two new sparse array configurations, named Zero-Redundancy Sparse Array (ZRSA-I and ZRSA-II), for direction-of-arrival (DOA) estimation. These arrays have been proven to possess the zero-redundancy property and offer more uniform degrees of freedom (uDOFs) compared to many existing sparse arrays. Furthermore, we propose the sum-difference Translation Invariance Criteria (SDTIC) to mitigate the mutual coupling (MC) effects in the arrays. Theoretical propositions and simulation results confirm the exceptional performance of the proposed ZRSAs, highlighting their potential for enhancing angle resolution and estimation accuracy.

关键词： Degrees of freedom DOA estimation sparse arrays sum-difference coarray zero-redundancy array

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Extended Nested Array with a Filled Sensor for DOA Estimation of Non-circular Signals

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Transactions of Nanjing University of Aeronautics and Astronautics 2025年第1期42卷 90-100页

作者： LI Xiaolong ZHANG Xiaofei SHEN Zihan College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)*** propose an extended nested array with a filled sensor(ENAFS)based on the hole-filling ***,we first introduce the improved nested array(INA)and prove its ***,we extend the sum-difference coarray(SDCA)by adding an additional sensor to fill the *** the larger uniform degrees of freedom(uDOFs)and virtual array aperture(VAA)can be abtained,and the ENAFS is ***,the simulation results are given to verify the superiority of the proposed ENAFS in terms of DOF,mutual coupling and estimation performance.

关键词： non-circular signal extended nested array sparse array direction of arrival(DOA)estimation sum-difference coarray

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New Array Designs for DoA Estimation of Non-Circular Signals With Reduced Mutual Coupling

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第7期72卷 8313-8328页

作者： Mohsen, Nabil Hawbani, Ammar Wang, Xingfu Bairrington, Benjamin Zhao, Liang Alsamhi, Saeed Univ Sci & Technol China Sch Comp Sci Hefei 230052 Peoples R China Univ Sci & Technol China Sch Math Hefei 230052 Peoples R China Shenyang Aerosp Univ Shenyang 110136 Peoples R China Univ Galway Insight Ctr Data Analyt Galway H91 TK33 Ireland IBB Univ Ibb 70270 Yemen

Recently, many sparse arrays are proposed to achieve increased uniform degrees of freedom (uDoFs) for the direction of arrival (DoA) estimation of non-circular sources. In light of this recent attention, the high number of uDoFs is the primary motivation for designing those sparse array structures. Although both the number of uDoF and mutual coupling can influence the DoA estimation performance. However, mutual coupling is not considered in many of those sparse array configurations. In this article, two optimized shifted coprime arrays with sum-difference coarrays, called OSCAwSDCa-I and OSCAwSDCa-II, are proposed to reduce mutual coupling. The OSCAwSDCa-I and OSCAwSDCa-II are obtained by rearranging actual sensors of the augmented coprime array. The proposed array designs have closed-form expressions for their sensor locations and achievable uDoFs. Additionally, it is proven that the first three weights of OSCAwSDCa-II are independent of the array size. Moreover, the number of virtual sensors co-locating at the same lag is reduced. Consequently, smaller weight values are obtained, resulting in more robustness against mutual coupling. Finally, extensive simulations show that OSCAwSDCa-I and OSCAwSDCa-II can achieve comparable uDoFs concerning the existing array structures. In the presence of strong mutual coupling, simulation results demonstrate the robustness of OSCAwSDCa-II more than other compared configurations.

关键词： Sparse array coprime array non-circular signals direction-of-arrival estimation sum-difference coarray mutual coupling

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DOA Estimation for Monostatic Coprime MIMO Radar With Mixed-Resolution Quantization

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第12期72卷 16737-16741页

作者： Wang, Lei Ren, Chunhui Zheng, Zhi Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China UESTC Inst Elect & Informat Engn Dongguan 523808 Guangdong Peoples R China

In this correspondence, we address the issue of direction-of-arrival (DOA) estimation for coprime multiple-input multiple-output (MIMO) radar employing mixed-resolution quantization. First, we derive the received signal model of coprime MIMO radar under mixed-resolution quantization, and utilize mixed-resolution measurements to construct an augmented signal vector via virtual array interpolation. Subsequently, we reconstruct a low-rank augmented covariance matrix without the effect of quantization error by solving an atomic norm minimization problem. Finally, we extract the DOAs of targets by applying MUSIC method on the reconstructed low-rank matrix. Unlike the existing techniques, our method not only recovers the missing correlation information, but also reduces the influence of noise and quantization error. Simulation results illustrate the advantage of the proposed approach over the existing techniques when handling mixed-resolution measurements.

关键词： DOA estimation Coprime MIMO radar mixed-resolution quantization sum-difference coarray interpolation

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Super Transformed Nested Arrays for DOA Estimation of Non-Circular Signals with Reduced Mutual Coupling 24

Super Transformed Nested Arrays for DOA Estimation of Non-Ci...

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3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Guo, Haodong Li, Jiajie Yi, Zelong Fang, Jiaxiong Chen, Hua Wang, Gang Ningbo Univ Ningbo Peoples R China

ISBN: (纸本)9798400709777

Recently, numerous sparse linear array structures have been developed to improve accuracy in Direction of Arrival (DOA) estimation for non-circular signals. The primary objective is to expand the continuous segment of the sum-difference coarray, thus increasing the uniform Degrees of Freedom (uDOFs). To balance a high number of uDOFs and low mutual coupling, a class of Super Transformed Nested Arrays (STNAs) is introduced based on the existing Transformed Nested Arrays (TNAs). By migrating certain sensors of original TNA, the mutual coupling issue caused by the dense subarray is alleviated. Following this scheme, two array structures are presented: 2-STNA and 3-STNA. Numerical results are provided to show the excellence of their performance in the presence of strong mutual coupling.

关键词： Direction of Arrival estimation nested array sum-difference coarray sparse array mutual coupling

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Optimized Sparse Nested Arrays for DoA Estimation of Non-circular Signals

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SIGNAL PROCESSING 2023年 204卷

作者： Mohsen, Nabil Hawbani, Ammar Wang, Xingfu Agrawal, Monika Zhao, Liang Univ Sci & Technol China Sch Comp Sci Hefei Peoples R China Indian Inst Technol Delhi Ctr Appl Res Elect New Delhi 110016 India Shenyang Aerosp Univ Shenyang Peoples R China

The recent criteria for designing sparse arrays used in many applications are the number of degrees of freedom (DoFs) and mutual coupling. In this paper, two optimized sparse nested arrays, called OSNAI and OSNA-II, are proposed for the direction of arrival (DoA) estimation of non-circular signals. The proposed structures aim to enhance the DoFs and mitigate mutual coupling. The OSNA-I and OSNA-II are obtained by rearranging physical sensors of the conventional nested array. Specifically, for the proposed OSNA-I, its dense uniform linear array (ULA) is exactly the same as the dense ULA of the conventional nested array, while the physical sensors of its sparse ULA are located at n (2 N 1 + 1) d. For the proposed OSNA-II, it is composed of two subarrays plus one separate sensor that is appropriately placed. The OSNAII keeps the dense array of the conventional nested array untouched except the sensor at N 1 d being removed, while its sparse array is deployed according to n (2 N 1 ) d. The separate sensor is set apart with the rightmost sensor of the sparse array by a space of 3 N 1 d. The closed-form expressions are derived for the actual sensor locations, number of uniform DoFs and available DoFs, respectively. Besides, it is also proven that OSNA-I and OSNA-II have small weight values due to their sparse structures, which means that OSNA-I and OSNA-II can achieve a good balance between DoFs and weight values. Simulation results demonstrate the superiorities of the proposed structures in terms of robustness and estimation accuracy.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Sparse array Nested array Non-circular signals sum-difference coarray Direction-of-arrival estimation Mutual coupling

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