Nonlinear Time-Varying Weak Signal Enhancement Method Based on Particle Filter

Traditional weak signal enhancement methods have low signal-to-noise ratio, which affects the accuracy of weak signal recognition. Therefore, this paper proposes a non-linear time-varying weak signal enhancement method based on particle filter. Collect nonlinear time-varying weak signals, extract nonlinear time-varying diffusion coefficients using particle filter, simulate the real distributed sampling process, build an adaptive neural network model, use subtractive clustering algorithm to determine the selection and number of hidden layer neuron centers, and improve the weak signal enhancement mode. The experimental results show that the signal-to-noise ratio of this method is up to 37.303 db, which shows that the nonlinear time-varying weak signal enhancement method designed by combining particle filter algorithm is more effective.