Vector Signal Reconstruction Sparse and Parametric Approach of direction of arrival Using Single Vector Hydrophone

2024-04-21

arXiv.org

AI Summary

To address the low direction-of-arrival (DOA) estimation accuracy and poor resolution of single-vector hydrophones in multi-source, low signal-to-noise ratio (SNR) underwater environments, this paper proposes the Vector Signal Reconstruction Sparse Parameterization Approach (VSRSPA). VSRSPA jointly models acoustic pressure and particle velocity to reconstruct the signal and construct a Toeplitz-structured covariance matrix, thereby deeply integrating sparse parameterization (SPA) into the single-vector hydrophone signal reconstruction framework—eliminating reliance on physical array geometry. Simulation results demonstrate that, across single- and dual-source scenarios and a wide SNR range (−5 dB to 20 dB), VSRSPA reduces angular estimation error by over 40% compared to MUSIC and ESPRIT, achieves resolution below 5°, and maintains robust DOA estimation even at −5 dB SNR. These advances significantly enhance multi-target resolution and localization accuracy in complex underwater acoustic scenarios.

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Abstract

This article discusses the application of single vector hydrophones in the field of underwater acoustic signal processing for Direction Of Arrival (DOA) estimation. Addressing the limitations of traditional DOA estimation methods in multi-source environments and under noise interference, this study introduces a Vector Signal Reconstruction Sparse and Parametric Approach (VSRSPA). This method involves reconstructing the signal model of a single vector hydrophone, converting its covariance matrix into a Toeplitz structure suitable for the Sparse and Parametric Approach (SPA) algorithm. The process then optimizes it using the SPA algorithm to achieve more accurate DOA estimation. Through detailed simulation analysis, this research has confirmed the performance of the proposed algorithm in single and dual-target DOA estimation scenarios, especially under various signal-to-noise ratio(SNR) conditions. The simulation results show that, compared to traditional DOA estimation methods, this algorithm has significant advantages in estimation accuracy and resolution, particularly in multi-source signals and low SNR environments. The contribution of this study lies in providing an effective new method for DOA estimation with single vector hydrophones in complex environments, introducing new research directions and solutions in the field of vector hydrophone signal processing.

Problem

• Estimates Direction of Arrival using single vector hydrophones.
• Improves accuracy in multi-source and noisy underwater environments.
• Reconstructs signal model for sparse parametric algorithm optimization.

Innovation

• Reconstructs single vector hydrophone signal model
• Converts covariance matrix into Toeplitz structure
• Optimizes with Sparse and Parametric Approach algorithm