Paper Info
Title
Coupled Coarray Tensor Cpd For Doa Estimation With Coprime L-Shaped Array
Abstract
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.
Year
DOI
Venue
2021
10.1109/LSP.2021.3099074
IEEE SIGNAL PROCESSING LETTERS
Keywords
DocType
Volume
Tensors, Estimation, Direction-of-arrival estimation, Array signal processing, Sensor arrays, Antenna arrays, Geometry, Coarray tensor, coprime L-shaped array, coupled CPD, DOA estimation
Journal
28
ISSN
Citations 
PageRank 
1070-9908
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Hang Zheng101.01
Zhiguo Shi224626.13
Chengwei Zhou382.86
Martin Haardt43531311.32
Jian Chen500.68