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Coupled Coarray Tensor CPD for DOA Estimation With Coprime L-Shaped Array
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DOI：10.1109/LSP.2021.3099074.png)
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Abstract
En 中文
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.
Keywords:
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
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