TY - JOUR
T1 - Bistable stochastic resonance with linear amplitude response enhanced vector DOA estimation under low SNR conditions
AU - Suo, Jian
AU - Dong, Haitao
AU - Shen, Xiaohong
AU - Wang, Haiyan
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Acoustic vector sensor (AVS)
KW - Bistable nonlinear model
KW - Linear amplitude response
KW - Stochastic resonance (SR)
KW - Vector DOA estimation
UR - http://www.scopus.com/inward/record.url?scp=85084084441&partnerID=8YFLogxK
U2 - 10.1016/j.chaos.2020.109825
DO - 10.1016/j.chaos.2020.109825
M3 - 文章
AN - SCOPUS:85084084441
SN - 0960-0779
VL - 136
JO - Chaos, Solitons and Fractals
JF - Chaos, Solitons and Fractals
M1 - 109825
ER -