Tensor Decomposition Enhanced LRFS Multi-DOA Tracking Based on Coprime Array

Liang Tao

doi:10.1109/TAES.2026.3652041

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Tensor Decomposition Enhanced LRFS Multi-DOA Tracking Based on Coprime Array

2026-01-12

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DOI：10.1109/TAES.2026.3652041

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Abstract

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This article addresses the problem of tracking a time-varying number of multiple direction-of-arrival (multi-DOA) by exploiting the received signals of a coprime array. The multi-DOA state is represented by a labeled random finite set, which is further modeled using the generalized labeled multi-Bernoulli (GLMB) distribution. Therefore, the GLMB filter naturally arises as a suitable method to propagate the multi-DOA posterior, from which the multi-DOA state can be extracted. To capture the intrinsic high-dimensional characteristics of the signals received by the coprime array, a tensor model is adopted, followed by the canonical polyadic decomposition to extract the rank-1 components of the tensor, which contain the spatial information corresponding to each source. Building on such a result, we formulate the multi-DOA likelihood in the form of a von Mises–Fisher distribution. This likelihood enables the derivation of the update equation for the GLMB filter. Furthermore, we developed a particle-based implementation of the algorithm, and its effectiveness is subsequently assessed through simulation experiments. Even under low signal-to-noise ratio and limited snapshot conditions, the proposed algorithm can accurately track the multi-DOA of time-varying source numbers, with the generalized optimal subpattern assignment error remaining within $0.5^\circ$.

Keywords:

Canonical polyadic decomposition (CPD)

generalized labeled multi-Bernoulli (GLMB filter

multi-direction-of-arrival (DOA) tracking

labeled random finite sets (LRFSs)

von Mises–Fisher distribution (vMF)