Joint Angle and Gain-Phase Error Estimation for Nested Bistatic MIMO Radar via Tensor Decomposition

Ren, Mingjian; Hu, Guoping; Shi, Junpeng; Zhou, Hao

doi:10.1016/j.sigpro.2022.108740

Cited by 9 publications

(1 citation statement)

References 31 publications

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“…where θ 0 denotes the DOA of target signal, which can be obtained using existing algorithms [40,41]. θ i denotes the DOA of the ith interference signal, and…”

Section: Signal Modelmentioning

confidence: 99%

ADMM-Net for Beamforming Based on Linear Rectification with the Atomic Norm Minimization

Gong,

Zhang,

Ren

et al. 2023

Remote Sensing

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Target misalignment can cause beam pointing deviations and degradation of sidelobe performance. In order to eliminate the effect of target misalignment, we formulate the jamming sub-space recovery problem as a linearly modified atomic norm-based optimization. Then, we develop a deep-unfolding network based on the alternating direction method of multipliers (ADMM), which effectively improves the applicability and efficiency of the algorithm. By using the back-propagation process of deep-unfolding networks, the proposed method could optimize the hyper-parameters in the original atomic norm. This feature enables the adaptive beamformer to adjust its weight according to the observed data. Specifically, the proposed method could determine the optimal hyper-parameters under different interference noise matrix conditions. Simulation results demonstrate that the proposed network could reduce computational cost and achieve near-optimal performance with low complexity.

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“…where θ 0 denotes the DOA of target signal, which can be obtained using existing algorithms [40,41]. θ i denotes the DOA of the ith interference signal, and…”

Section: Signal Modelmentioning

confidence: 99%