Dong Haitao, Ma Shilei, Suo Jian, Zhu Zhigang
Xi'an Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xidian University, Xi'an 710071, China.
Shaanxi Union Research Center of University and Enterprise for Intelligent Spectrum Sensing and Information Fusion, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2024 May 6;24(9):2943. doi: 10.3390/s24092943.
Remote passive sonar detection with low-frequency band spectral lines has attracted much attention, while complex low-frequency non-Gaussian impulsive noisy environments would strongly affect the detection performance. This is a challenging problem in weak signal detection, especially for the high false alarm rate caused by heavy-tailed impulsive noise. In this paper, a novel matched stochastic resonance (MSR)-based weak signal detection model is established, and two MSR-based detectors named MSR-PED and MSR-PSNR are proposed based on a theoretical analysis of the MSR output response. Comprehensive detection performance analyses in both Gasussian and non-Gaussian impulsive noise conditions are presented, which revealed the superior performance of our proposed detector under non-Gasussian impulsive noise. Numerical analysis and application verification have revealed the superior detection performance with the proposed MSR-PSNR detector compared with energy-based detection methods, which can break through the high false alarm rate problem caused by heavy-tailed impulsive noise. For a typical non-Gasussian impulsive noise assumption with α=1.5, the proposed MSR-PED and MSR-PSNR can achieve approximately 16 dB and 22 dB improvements, respectively, in the detection performance compared to the classical PED method. For stronger, non-Gaussian impulsive noise conditions corresponding to α=1, the improvement in detection performance can be more significant. Our proposed MSR-PSNR methods can overcome the challenging problem of a high false alarm rate caused by heavy-tailed impulsive noise. This work can lay a solid foundation for breaking through the challenges of underwater passive sonar detection under non-Gaussian impulsive background noise, and can provide important guidance for future research work.
利用低频带谱线的远程被动声纳探测备受关注，而复杂的低频非高斯脉冲噪声环境会严重影响探测性能。这是弱信号检测中的一个具有挑战性的问题，尤其是对于由重尾脉冲噪声导致的高误报率而言。本文建立了一种基于匹配随机共振（MSR）的新型弱信号检测模型，并基于对MSR输出响应的理论分析，提出了两种基于MSR的探测器，即MSR - PED和MSR - PSNR。给出了在高斯和非高斯脉冲噪声条件下的综合探测性能分析，结果表明我们提出的探测器在非高斯脉冲噪声下具有卓越性能。数值分析和应用验证表明，与基于能量的检测方法相比，所提出的MSR - PSNR探测器具有卓越的探测性能，能够突破由重尾脉冲噪声引起的高误报率问题。对于α = 1.5的典型非高斯脉冲噪声假设，与经典的PED方法相比，所提出的MSR - PED和MSR - PSNR在探测性能上分别可实现约16 dB和22 dB的提升。对于对应α = 1的更强的非高斯脉冲噪声条件，探测性能的提升可能更为显著。我们提出的MSR - PSNR方法能够克服由重尾脉冲噪声导致的高误报率这一具有挑战性的问题。这项工作可为突破非高斯脉冲背景噪声下的水下被动声纳探测挑战奠定坚实基础，并可为未来的研究工作提供重要指导。
