Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars

Abstract

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).

Control of stochastic and inverse stochastic resonances in a liquid-crystal electroconvection system using amplitude and phase noises

“…If there exists a nonequilibrium, free energy-like potential with its minima at  = 0 and  ≠ 0 (i.e., a rest state and a convection state, respectively), which correspond to an ice state and a warm state, respectively, in the study of iceage cycles [2,3], such a potential should be investigated along with its symmetry breaking [1,19] to understand the detail of the competition mechanism; and this is an important open question. Our numerical and experimental findings suggest that the control of SR and ISR by combining amplitude and phase noises can be very useful for electrical applications, such as sensing technologies [36,49,50] and brain science [12,13], for which additional phase noise can be readily introduced. Furthermore, the transition between SR and ISR may provide effective controls for desired and undesired performances in various related fields [51,52].…”

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

“…From Equation (38), we can see that the Kramers rate (r K ) is related to the output power spectra (G(ω)), the system potential parameters (a and b), noise intensity (D), and driving frequency ( f 0 ). Therefore, for the periodic input with additive Gaussian noise in Equation (31), the input SNR of the system can be expressed as [42]…”

“…Upon combining Equations ( 37) and (38), the SNR output corresponding to Equation (31) can be finally approximated as [41]…”

“…Therefore, it has attracted considerable attention in a variety of research fields [32][33][34][35]. In our previous work, a matched stochastic resonance framework was proposed for remote passive sonar detection [35][36][37][38]. This work can effectively improve weak-signal detection performance, especially under low-SNR circumstances.…”

Remote Sensing in Vessel Detection and Navigation

“…Ground users could be informed about the detected ships within minutes after their detection without a direct communication link. Dong et al [11] address the problem of passive sonar detection and identification in low signal to noise rate conditions. They proposed adaptive intrawell matched stochastic resonance Adaptive Linear Amplifiers (AIMSR) method, aiming to break through the limitation of the conventional adaptive line enhancers (ALE) method by nonlinear filtering effects.…”