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SNR gain enhancement in a generalized matched filter using artificial optimal noise

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  • Ren, Yuhao
  • Pan, Yan
  • Duan, Fabing

Abstract

For a weak signal buried in a given background noisy environment, a generalized matched filter composed of nonlinearities and weight coefficients is investigated by exploring the potential benefit of the artificial noise. With the output signal-to-noise ratio (SNR) of the conventional linear matched filter as a benchmark, the SNR gain of the generalized matched filter is proven to be possibly improved by adding an optimal noise into the easily implemented nonlinearity. From the practical point of view, even if the filter itself has an adaptability to the noisy environment, the approach of adaptive stochastic resonance still finds an optimal non-zero amount of added noise to enhance the SNR gain of the generalized matched filter. Interestingly, the optimal added noise found by the adaptive stochastic resonance method can also maximize another meaningful measure of the mean residence time (MRT), which provides more physical insight to the generalized matched filter in the sense of the noise-enhanced stability phenomenon. These obtained results indicate an extended application of the incorporation of noise in the nonlinear filter design.

Suggested Citation

  • Ren, Yuhao & Pan, Yan & Duan, Fabing, 2022. "SNR gain enhancement in a generalized matched filter using artificial optimal noise," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    • Handle: RePEc:eee:chsofr:v:155:y:2022:i:c:s096007792101095x
    DOI: 10.1016/j.chaos.2021.111741
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