ISSN:
1070-9908
Content:
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.
In:
Institute of Electrical and Electronics Engineers, IEEE signal processing letters, New York, NY : IEEE, 1994, 28(2021), Seite 1545-1549, 1070-9908
In:
volume:28
In:
year:2021
In:
pages:1545-1549
Language:
English
DOI:
10.1109/LSP.2021.3099074
Author information:
Haardt, Martin
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