Improved Frequency Detection Capability of MEMS Bionic Vector Hydrophone in Low Signal-to-Noise Ratio Environment

Abstract

With the development of underwater target noise reduction technologies, the detection capabilities of microelectromechanical system (MEMS) bionic vector hydrophones (BVHs) are encountering significant challenges in environments characterized by a low signal-to-noise ratio (SNR). Thus, this article proposes an innovative time-reversal convolution (TRC) processing method for both sound pressure and velocity based on the output characteristics of MEMS BVHs. This methodology capitalizes on the distinctions between signal and noise postconvolution processing, employing a subsequent stage of adaptive line enhancement (ALE) technology to adeptly mitigate ambient interference, which, in turn, markedly augments the detection capabilities within low SNR. In addition, addressing the challenge of broad main lobes in the directional pattern and the reduced precision in bearing estimation for single vector sensors during the processing of pressure and velocity signals, an innovative direction-of-arrival (DOA) estimation technique has been introduced. This technique employs adaptive cancellation of the outputs from TRC processing, enhancing the accuracy and reliability of the bearing estimation. Utilizing Monte Carlo simulations, this study meticulously examined the gain fluctuations in response to varying input SNRs and then meticulously contrasted them with those of existing integrated methodologies to evaluate their comparative effectiveness. The simulation results demonstrate that the proposed methodology is capable of markedly amplifying the detection efficacy for low-intensity underwater targets within environments of diminished SNR. The potency of this approach in bolstering the operational prowess of MEMS BVHs is corroborated through empirical validation with actual test data.