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Bistable stochastic resonance with linear amplitude response enhanced vector DOA estimation under low SNR conditions

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  • Suo, Jian
  • Dong, Haitao
  • Shen, Xiaohong
  • Wang, Haiyan

Abstract

This work demonstrates a superior vector DOA estimation results by linear amplitude response of stochastic resonance with bistable nonlinear model, especially under low SNR conditions. The pre-processing problem of classical intensity based vector DOA estimation method is theoretically analyzed with gain-phase uncertainties, which demonstrate a constraint of linear amplitude response with a certain phase shift for an unbiased estimate of the true azimuth. In this way, linear amplitude response stochastic resonance is parametric modeled with gain-phase constraint, and achieved by the matched stochastic resonance theory with a maximized output SNR and a steady phase lag of π/2. The linear relation between the input and the output amplitude is simulative analyzed under different SNR conditions, which reflect a good linearity with the input amplitude A0 < 1. In contrary to the state-of-art complex acoustic intensity measurement (CAIM) method, a great improvement on estimation performance can be achieved, especially under low SNR conditions. This allows us a new point of view to enhance the vector DOA estimation in the assistance of nonlinear bistable SR effect, and can be a breakthrough innovation guidance for underwater acoustic remote sensing with vector sensors in the future.

Suggested Citation

  • Suo, Jian & Dong, Haitao & Shen, Xiaohong & Wang, Haiyan, 2020. "Bistable stochastic resonance with linear amplitude response enhanced vector DOA estimation under low SNR conditions," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:chsofr:v:136:y:2020:i:c:s0960077920302253
    DOI: 10.1016/j.chaos.2020.109825
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    References listed on IDEAS

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    1. Dong, Haitao & Shen, Xiaohong & He, Ke & Wang, Haiyan, 2020. "Nonlinear filtering effects of intrawell matched stochastic resonance with barrier constrainted duffing system for ship radiated line signature extraction," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    2. Yong Xu & Juanjuan Li & Jing Feng & Huiqing Zhang & Wei Xu & Jinqiao Duan, 2013. "Lévy noise-induced stochastic resonance in a bistable system," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(5), pages 1-7, May.
    3. A. Dubkov & B. Spagnolo, 2008. "Verhulst model with Lévy white noise excitation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 65(3), pages 361-367, October.
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    Cited by:

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    2. Wang, Guancheng & Li, Qinrou & Liu, Shaoqing & Xiao, Hua & Zhang, Bob, 2022. "New zeroing neural network with finite-time convergence for dynamic complex-value linear equation and its applications," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. Dong, Haitao & Shen, Xiaohong & He, Ke & Wang, Haiyan, 2020. "Nonlinear filtering effects of intrawell matched stochastic resonance with barrier constrainted duffing system for ship radiated line signature extraction," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).

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