Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

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Abstract
Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work.
Funding Information
  • National Natural Science Foundation of China (62201439, 62031021, 62276204, 62203343, 2022M722493, XJSJ23026, 2023BSHEDZZ174)
  • China Postdoctoral Science Foundation (62201439, 62031021, 62276204, 62203343, 2022M722493, XJSJ23026, 2023BSHEDZZ174)
  • Fundamental Research Funds for the Central Universities (62201439, 62031021, 62276204, 62203343, 2022M722493, XJSJ23026, 2023BSHEDZZ174)
  • Postdoctoral Research Project of Shannxi Province (62201439, 62031021, 62276204, 62203343, 2022M722493, XJSJ23026, 2023BSHEDZZ174)

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