Parameters estimate of recurrent quantum stochastic filter for time variant frequency periodic signals

Abstract

Designing optimal time and spatial difference step size is the key technology for quantum-random filtering (QSF) to realize time-varying frequency periodic signal filtering. In this paper, it was proposed to use the short-time Fourier transform (STFT) to dynamically estimate the signal to noise ratio (SNR) and relative frequency of the input time-varying frequency periodic signal. Then the model of time and space difference step size and signal to noise ratio (SNR) and relative frequency of quantum random filter is established by least square method. Finally, the parameters of the quantum filter can be determined step by step by analyzing the characteristics of the actual signal. The simulation results of single-frequency signal and frequency time-varying signal show that the proposed method can quickly and accurately design the optimal filter parameters based on the characteristics of the input signal, and achieve significant filtering effects.

Keywords

quantum stochastic filter (QSF) / parameters estimation / least square (LS) / short-time Fourier transform (STFT)

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