Spatial-Resampling Wideband Compressive Beamforming

Zhu, Zhen; Chi, Cheng; Zhang, Chunhua

doi:10.23919/oceans44145.2021.9706024

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Its appearance improves the performance of high-resolution DOA estimation under low sampling conditions. Compressive beamforming methods have higher angular resolution than traditional high-resolution beamforming methods [9].Furthermore, the case where the number of sources is larger than the number of physical sensors has been shown to be much more difficult in the DOA estimation problems studied [10,11]. For this uncertain DOA estimation task, various sparse array structures have been proposed as 2 of 18 possible solutions [12,13], such as the nested array [14,15] and coprime array [16] and their various extensions, for both second-order and fourth-order problems.…”

Section: Introductionmentioning

confidence: 99%

An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix

Sun

Wen

et al. 2022

Applied Sciences

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The compressive sensing method is an effective way of under-sampling array signal processing, and the measurement matrix is a key technology of compressive sensing processing, which is of decisive significance to improve the performance of under-sampling array signal processing. In this paper, an under-sampling array signal processing method based on an improved Hadamard measurement matrix was proposed. This method improved the construction of the measurement matrix by a compressing zero method capable of reducing the redundant data, enhancing the non-correlation between columns, improving the RIP condition, and improving the performance of the compressive sensing method. The performance was verified by simulation and real data from the lake, and the results showed that the method proposed in this paper has obvious performance advantages compared with the Toeplitz Circular Matrix, the m-sequence-based matrix, and the partial Hadamard matrix under the same conditions.

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