检索结果-内蒙古大学图书馆

virtual array interpolation for 2-D DOA and Polarization Estimation Using Coprime EMVS array via Tensor Nuclear Norm Minimization

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 3637-3650页

作者： Fu, Mingcheng Zheng, Zhi Wang, Wen-Qin So, Hing Cheung Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

In this article, we develop an interpolation-based algorithm for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation with coprime electromagnetic vector-sensor (EMVS) array. First of all, we derive the tensor form coarray output of coprime EMVS array, and perform virtual array interpolation on the output components of the difference coarray. Subsequently, we construct a low-rank third-order augmented tensor using the interpolated uniform linear array output, and derive two important properties for this low-rank tensor in the Fourier domain. Based on these properties, we reconstruct a noise-free third-order augmented tensor by formulating a tensor nuclear norm (TNN) minimization problem. Finally, we derive the closed-form expressions of 2-D DOA and polarization estimates using the reconstructed tensor. Unlike the existing techniques, our approach not only avoids losses in array aperture and degrees-of-freedom, but also exploits the multidimensional structure inherent in the coarray output. Numerical results demonstrate the superiority of the proposed algorithm over the existing approaches.

关键词： Coprime EMVS array two-dimensional (2-D)direction-of-arrival (DOA) estimation polarization estimation virtual array interpolation tensor nuclear norm (TNN)

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Direction-of-Arrival Estimation for Coprime array via virtual array interpolation

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第22期66卷 5956-5971页

作者： Zhou, Chengwei Gu, Yujie Fan, Xing Shi, Zhiguo Mao, Guoqiang Zhang, Yimin D. Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA Univ Technol Sydney Sch Comp & Commun Sydney NSW 2007 Australia

Coprime arrays can achieve an increased number of degrees of freedom by deriving the equivalent signals of a virtual array. However, most existing methods fail to utilize all information received by the coprime array due to the non-uniformity of the derived virtual array, resulting in an inevitable estimation performance loss. To address this issue, we propose a novel virtual array interpolation-based algorithm for coprime array direction-of-arrival (DOA) estimation in this paper. The idea of array interpolation is employed to construct a virtual uniform linear array such that all virtual sensors in the non-uniform virtual array can be utilized, based on which the atomic norm of the second-order virtual array signals is defined. By investigating the properties of virtual domain atomic norm, it is proved that the covariance matrix of the interpolated virtual array is related to the virtual measurements under the Hermitian positive semi-definite Toeplitz condition. Accordingly, an atomic norm minimization problem with respect to the equivalent virtual measurement vector is formulated to reconstruct the interpolated virtual array covariance matrix in a gridless manner, where the reconstructed covariance matrix enables off-grid DOA estimation. Simulation results demonstrate the performance advantages of the proposed DOA estimation algorithm for coprime arrays.

关键词： Atomic norm coprime array direction-of-arrival estimation gridless Toeplitz matrix reconstruction virtual array interpolation

来源：评论

学校读者我要写书评

暂无评论

Direct position determination of multiple sources using a moving virtual interpolation array

引用

DIGITAL SIGNAL PROCESSING 2025年 159卷

作者： Wang, Zhaobo Guo, Hui Miao, Yingjie Zhang, Jun Nanjing Elect Equipment Inst Nanjing 210000 Peoples R China

Direct position determination (DPD) refers to determining the target position directly without estimating intermediate positioning parameters. Compared to the traditional two-step methods, it avoids parameter correlations and significantly enhances the algorithm's adaptability to low Signal-to-Noise Ratio (SNR) conditions. This paper uses coprime arrays to investigate direct positioning in a motion single-station passive localization system. Addressing issues where current algorithms fail to fully utilize array aperture and perform poorly in low snapshot scenarios, this paper proposes a motion single-station DPD algorithm based on virtual interpolated arrays. The proposed algorithm first uses the l0 atomic norm to estimate the covariance matrix after filling gaps in the difference co-array. Then, the MVDR (Minimum Variance Distortionless Response) method is applied to fuse covariance estimates for localization. Additionally, we derive the Cram & eacute;r-Rao lower bound. Numerical simulations validate the algorithm's performance, demonstrating its ability to maximize the degrees of freedom provided by coprime arrays and achieve superior performance in scenarios with short snapshots.

关键词： Direct position determination (DPD) Coprime array virtual array interpolation Gridless Toeplitz matrix reconstruction Atomic norm

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Direct Position Determination of Mixed Circular and Non-Circular Sources Using Moving virtual interpolation array

引用

SENSORS 2024年第20期24卷 6718页

作者： Wang, Zhaobo Zhang, Jun Guo, Hui Miao, Yingjie Nanjing Elect Equipment Inst Nanjing 210000 Peoples R China

In this study, a moving single-station direct position determination (DPD) algorithm based on virtual interpolated arrays is proposed. Existing moving single-station algorithms face challenges such as the incomplete utilization of sparse array apertures and insufficient consideration of mixed circular and non-circular signals. To address these issues, we propose an enhanced gridless DPD algorithm, suitable for multiple mixed circular and non-circular sources. Through constructing a non-zero unconjugated covariance matrix from the non-circular components of the mixed signals, the data dimensionality is expanded, and the gridless method is used to fill the voids in the coarray, significantly improving localization performance. Additionally, a unitary transformation method is applied to reduce computational complexity. This method transforms complex operations into real operations by applying unitary transformations to steering vectors and subspaces. Simulation results demonstrate that the proposed algorithm offers significant advantages in terms of array degrees of freedom and localization accuracy.

关键词： direct position determination (DPD) sparse array non-circular and circular signals virtual array interpolation low-rank structured covariance reconstruction (LRSCR) unitary transformation method

来源：评论

学校读者我要写书评

暂无评论

Co-Prime array interpolation for DOA Estimation Using Deep Matrix Iterative Network

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Su, Yunye Wang, Xianpeng Lan, Xiang 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

Co-prime arrays achieve a significant number of virtual arrays in the same physical sensors by different co-arrays. There are, however, many existing methods that do not utilize all the information that the coprime array receives due to the difference in co-array having defects that cause empty holes in the virtual arrays. To estimate the direction of arrival (DOA), we present in this article an algorithm for co-prime array interpolation based on deep learning (DL). The proposed interpolation algorithm employs the covariance matrix of the interpolated virtual array to construct a self-supervision loss function based on the Hermitian semi-definite Toeplitz condition. And build a novel net structure to learn the mapping of the loss function described above. First, we build a matrix iterative network (MIN) by the idea of array interpolation such that all the information of the virtual array can be utilized. Subsequently, we fill in zero elements in each empty hole of virtual arrays, put it into the MIN, and receive the interpolated covariance matrix. By exploiting MIN, we recover the covariance matrix for DOA estimation. The simulation performance and experimental result have verified the superiority of the proposed algorithm.

关键词： Co-array interpolation co-prime array deep learning (DL) direction-of-arrival (DOA) estimation matrix reconstruction neural networks (NNs) virtual array interpolation Co-array interpolation co-prime array deep learning (DL) direction-of-arrival (DOA) estimation matrix reconstruction neural networks (NNs) virtual array interpolation

来源：评论

学校读者我要写书评

暂无评论

interpolation-Based Direction-of-Arrival Estimation for Coprime arrays via Covariance Matrix Fitting

引用

IEEE ACCESS 2020年 8卷 149133-149141页

作者： Chen, Zhen Fan, Chongyi Huang, Xiaotao Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

Coprime arrays can highly increase degree-of-freedom (DOF) by exploiting the equivalent virtual signal. However, since the corresponding virtual array constructed by the coprime array is always a non-uniform linear array (non-ULA), most existing direction-of-arrival (DOA) estimation algorithms fail to utilize all received information and result in performance degradation. To address this issue, we propose a novel interpolation approach for coprime arrays to convert the virtual array into a ULA with which all received information can be efficiently utilized. In this paper, we consider a weighted covariance matrix fitting criterion to formulate a semi-definite programming (SDP) problem with respect to the interpolated virtual signal. After that, we can reconstruct a Hermitian Toeplitz covariance matrix corresponding to the interpolated ULA in a gridless manner, and the number of detectable targets is ulteriorly increased with the reconstructed covariance matrix. The proposed approach is hyperparameter-free so that the tedious process of selecting regularization parameters is avoided. Numerical experiments validate the superiority of the proposed interpolation-based DOA estimation algorithm in terms of DOF characteristic, resolution ability and estimation accuracy compared with several existing techniques.

关键词： Sensor arrays Covariance matrices Direction-of-arrival estimation Estimation interpolation array signal processing Coprime arrays DOA estimation virtual array interpolation covariance matrix fitting hyperparameter-free

来源：评论

学校读者我要写书评

暂无评论

Robust Direction-of-Arrival Estimation for Coprime array in the Presence of Miscalibrated Sensors

引用

IEEE ACCESS 2020年 8卷 27152-27162页

作者： Kou, Jiaxun Li, Ming Jiang, Chunlan Beijing Inst Technol State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China

Coprime arrays have been widely adopted for direction-of-arrival (DOA) estimation since it can achieve an increased number of degrees of freedom (DOF). To utilize all information received by the coprime array, array interpolation methods are developed, which construct a virtual uniform linear array (ULA) with the same aperture from the non-uniform coprime array. However, the conventional non-robust DOA estimation algorithms for coprime arrays, including the interpolation based methods, suffer from degraded performance or even failed operation when some sensors are miscalibrated. In this paper, a novel maximum correntropy criterion (MCC) based virtual array interpolation algorithm for robust DOA estimation is developed to address this problem. The proposed approach treats the miscalibrated sensor observations as outliers, and by exploiting the property of MCC, the interpolated virtual array covariance matrix is reconstructed via nuclear norm minimization (NNM) with less influence of these outliers. In this manner, the robust DOA estimation is enabled by the robustly reconstructed covariance matrix. Simulation results demonstrate that the proposed algorithm can effectively the mitigate effect of the miscalibrated sensors while maintaining the enhanced DOF offered by coprime arrays.

关键词： Coprime array calibration error virtual array interpolation maximum correntropy criterion (MCC) robust direction-of-arrival (DOA) estimation

来源：评论

学校读者我要写书评

暂无评论

M-Estimator Based Robust Coarray interpolation for DOA Estimation with Miscalibrated Sensors 3

M-Estimator Based Robust Coarray Interpolation for DOA Estim...

引用

3rd International Conference on Unmanned Systems (ICUS)

作者： Kou, Jiaxun Jiang, Chunlan Li, Ming Beijing Inst Technol Sch Mechatron Engn Beijing Peoples R China

ISBN: (纸本)9781728180250

This paper proposes a robust signal interpolation approach for direction-of-arrival (DOA) estimation when several sensors in a coprime array are not properly calibrated. In such case, the conventional interpolation approaches will lead to an inaccurate or even failed DOA estimation. In our proposed approach, observations obtained from the sensors without calibration are blindly treated as outliers. The interpolation problem is formulated as an atomic norm minimization (ANN") problem, where the M-estimator is employed as the regularization term to reduce the adverse impact of outliers. The DOA estimation results can be thus robustly obtained by applying the interpolated virtual signal matrix. Numerical experiments verify that our interpolation algorithm outperformance existing array interpolation methods in terms of robustness.

关键词： Coprime array virtual array interpolation atomic norm minimization (ANM) calibration error M-estimator robust DOA estimation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：