School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry ofIndustry and Information Technology, Xi'an 710072, China.
Sensors (Basel). 2020 Jun 8;20(11):3269. doi: 10.3390/s20113269.
Remote passive sonar detection and classification are challenging problems that require the user to extract signatures under low signal-to-noise (SNR) ratio conditions. Adaptive line enhancers (ALEs) have been widely utilized in passive sonars for enhancing narrowband discrete components, but the performance is limited. In this paper, we propose an adaptive intrawell matched stochastic resonance (AIMSR) method, aiming to break through the limitation of the conventional ALE by nonlinear filtering effects. To make it practically applicable, we addressed two problems: (1) the parameterized implementation of stochastic resonance (SR) under the low sampling rate condition and (2) the feasibility of realization in an embedded system with low computational complexity. For the first problem, the framework of intrawell matched stochastic resonance with potential constraint is implemented with three distinct merits: (a) it can ease the insufficient time-scale matching constraint so as to weaken the uncertain affect on potential parameter tuning; (b) the inaccurate noise intensity estimation can be eased; (c) it can release the limitation on system response which allows a higher input frequency in breaking through the large sampling rate limitation. For the second problem, we assumed a particular case to ease the potential parameter a o p t = 1 . As a result, the computation complexity is greatly reduced, and the extremely large parameter limitation is relaxed simultaneously. Simulation analyses are conducted with a discrete line signature and harmonic related line signature that reflect the superior filtering performance with limited sampling rate conditions; without loss of generality of detection, we considered two circumstances corresponding to H 1 (periodic signal with noise) and H 0 (pure noise) hypotheses, respectively, which indicates the detection performance fairly well. Application verification was experimentally conducted in a reservoir with an autonomous underwater vehicle (AUV) to validate the feasibility and efficiency of the proposed method. The results indicate that the proposed method surpasses the conventional ALE method in lower frequency contexts, where there is about 10 dB improvement for the fundamental frequency in the sense of power spectrum density (PSD).
远程被动声纳探测和分类是具有挑战性的问题，需要用户在低信噪比（SNR）条件下提取特征。自适应线增强器（ALE）已广泛应用于被动声纳中，用于增强窄带离散分量，但性能有限。在本文中，我们提出了一种自适应腔内匹配随机共振（AIMSR）方法，旨在通过非线性滤波效应突破传统 ALE 的局限性。为了使其具有实际应用价值，我们解决了两个问题：（1）在低采样率条件下，随机共振（SR）的参数化实现问题；（2）在具有低计算复杂度的嵌入式系统中实现的可行性问题。对于第一个问题，实现了具有潜在约束的腔内匹配随机共振框架，具有三个显著优点：（a）可以缓解时间尺度匹配不足的约束，从而减轻潜在参数调整的不确定性影响；（b）可以缓解噪声强度估计不准确的问题；（c）可以放宽系统响应的限制，允许更高的输入频率来突破大采样率的限制。对于第二个问题，我们假设了一种特殊情况，以简化潜在参数 a o p t = 1 。结果，计算复杂度大大降低，同时放宽了对参数的限制。通过离散线特征和与谐波相关的线特征的仿真分析，在有限的采样率条件下，验证了其具有优越的滤波性能；不失一般性的检测，我们分别考虑了 H 1 （带噪声的周期性信号）和 H 0 （纯噪声）假设的两种情况，这很好地反映了检测性能。通过自主水下航行器（AUV）在水库中的实验验证，验证了该方法的可行性和效率。结果表明，该方法在较低频率环境下优于传统的 ALE 方法，在功率谱密度（PSD）意义上，基频的改善约为 10dB。
