A weak signal detection method for piecewise nonlinear bistable systems

Abstract

The invention relates to a weak signal detection method based on adaptive piecewise nonlinear bistable stochastic resonance, and belongs to the technical field of signal processing. In order to extract weak signal under strong noise background, the invention combines an exponential function and a classical bistable potential function model to propose a novel piecewise nonlinear bistable stochasticresonance system. The method based on this system firstly pretreats the noisy signal to satisfy the small parameter condition of adiabatic approximation theory, Then the searching range of the parameters of piecewise nonlinear bistable system is set according to the known conditions, and the parameters of piecewise nonlinear bistable system are optimized by using the average signal-to-noise ratiogain as the performance average index, so as to produce the best resonance effect. The piecewise nonlinear bistable potential well model proposed by the invention has the advantages of the exponential type single potential well model and the traditional bistable model, and has universal significance in practical application. Adaptive optimization algorithm is used to optimize the system parameters, which avoids the poor resonance effect caused by the poor selection of system parameters.

Description

Technical field [0001] The invention belongs to the technical field of signal processing, and is specifically a weak signal detection method based on adaptive piecewise nonlinear bistable stochastic resonance. Background technique [0002] In recent years, for the noise in weak signals, the traditional processing methods include singular value decomposition (SVD) and wavelet transform (WT), but these methods are often harmful and unprofitable when the signal-to-noise ratio is extremely low. exist. Therefore, as Benzi et al. first proposed stochastic resonance when studying the problem of ancient meteorological glaciers, stochastic resonance entered people's field of vision as a new signal processing method. In the 1990s, Collins combined information theory with stochastic resonance and proposed a This non-periodic stochastic resonance theory broadens the application range of stochastic resonance. In a nonlinear system, within a certain range of signal-to-noise ratio, the stocha...

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Problems solved by technology

From the above potential well models, these potential well models have outstanding advantages compared with the more mature classical bistable stochastic resonance (CBSR) system; and through theoretical analysis, it can be known that the CBSR system has the same Inherent output saturation; this characteristic not only reduces the system's ability to enhance the signal, but also limits the system's ability to detect the signal in a noisy environment. Therefore, in the extraction and detection of weak signals, how to effectively avoid double The output saturation of the stable system is necessary. In order to overcome this output saturation, a novel piecewise nonlinear bistable stochastic resonance system (PNBSR) is proposed. Based on this model, a A Weak Signal Detection Method Based on Adaptive PNBSR

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