A multi-frame space-time coding method for the phase-coded signal in MIMO radars

Zhang, Cheng; Zhang, Gong

doi:10.1016/j.dsp.2022.103831

Cited by 2 publications

(1 citation statement)

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“…), and the number of samples must be greater than twice the system degrees of freedom. [3][4][5][6][7] The traditional sliding window (SW) method uses the reference cell samples on both sides of the CUT as training samples to perform maximum likelihood estimation of the CCM, but the results are usually inaccurate.…”

Section: Introductionmentioning

confidence: 99%

Sparse recovery space time adaptive processing based on log-determinant

Chang,

Du,

Jin

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Airborne radars usually face non-uniform clutter environments, and it is difficult to obtain enough independent and identical distributed (i.i.d.) training samples, which degrades the clutter suppression performance of space time adaptive processing (STAP). To address the problem, a log-determinant sparse rocovery based STAP (LSR-STAP) method is proposed in this paper. Through the sparse recovery theory of low-rank matrices and the prior knowledge of clutter covariance matrices, the corresponding problem is modeled using the log-determinant (LogDet) approximation of the rank function, and the solution of the resulting nonconvex optimization problem is derived in the framework of the symmetric alternating direction method of multipliers (S-ADMM). The simulation results show the superiority of the proposed method over similar algorithms.

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