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

Jian Suo, Haitao Dong, Xiaohong Shen and Haiyan Wang

Chaos, Solitons & Fractals, 2020, vol. 136, issue C

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.

Keywords: Stochastic resonance (SR); Linear amplitude response; Bistable nonlinear model; Acoustic vector sensor (AVS); Vector DOA estimation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077920302253
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:136:y:2020:i:c:s0960077920302253

DOI: 10.1016/j.chaos.2020.109825

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().