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Deconvolutional Port-Starboard Discrimination Algorithm Under Dual Linear Array Conditions
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DOI：10.1109/LSP.2026.3657241.png)
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Although beamforming design enables port-starboard discrimination to some extent, it performs poorly under low signal-to-noise ratio (SNR) conditions and exhibits low port-starboard suppression, leading to potential misclassification. To address the challenge of port-starboard discrimination in underwater acoustic detection using dual linear arrays, this letter proposes a novel discrimination algorithm based on dual linear array processing. The proposed algorithm first utilizes the Linearly Constrained Minimum Variance (LCMV) criterion to construct a beamforming vector and calculate the spatial spectrum of the received data, thereby obtaining a coarse estimation of the target's azimuth. Building on the beamforming process, we construct a dictionary matrix to establish the relationship between the spatial spectrum and the spatial power distribution vector. Furthermore, to mitigate the impact of sidelobes in the spatial spectrum, we design a set of projection operators to process the spatial spectrum and dictionary matrix, thereby obtaining a cleaner spatial relationship. In addition, to enhance numerical stability, we incorporate a regularization term into the objective function using the noise subspace. Simulations and experimental demonstrate that the proposed algorithm achieves an exceptionally high port-starboard suppression ratio and superior azimuth resolution capability.
Keyword:
Dual linear array
optimized beamforming
spatial power distribution vector
quadratic programming
deconvolution
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3.9
论文数:
784
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0
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