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Coarray Tensor Train Aided Target Localization for Bistatic MIMO Radar
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DOI：10.1109/LSP.2024.3495555.png)
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Abstract
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In this letter, a coarray Tensor Train (TT) decomposition method is proposed to locate the targets in bistatic Multiple-Input Multiple-Output (MIMO) radar with uniform planar array (UPA) geometry. Initially, a five-dimensional (5-D) tensor model is established to preserve the multi-dimensional structure in the receive part. Subsequently, a four-dimensional (4-D) low-rank tensor can be obtained by removing redundant elements in the difference coarray of the eight-dimensional (8-D) covariance tensor. The TT-SVD algorithm is then applied to convert this 4-D tensor into a sequential product of lower-order TT-cores. Compared to CANDECOMP/PARAFAC decomposition (CPD) and Tucker decomposition (TD), the advantages of TTD include flexible multi-way data representation and mitigation of the curse of dimensionality. Furthermore, by using the relationship between Vandermonde factors matrices and TT-cores, a new method by combining different TT-cores is devised to estimate the angle parameters. Simulation results confirm the superiority of the proposed TT-aided method over other tensor-based methods for bistatic MIMO radar.
Keywords:
Tensors
MIMO radar
Transmission line matrix methods
Matrix decomposition
Vectors
Planar arrays
Estimation
Covariance matrices
Location awareness
Geometry
Bistatic MIMO radar
coarray tensor
angle parameter estimation
tensor train decomposition (TTD)
TT-SVD algorithm
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9.6
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1.7W
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