Extracting Periodic Features and Immunity of Electronic Communication Signals Using Adaptive Filtering Algorithms

Abstract

With the increasing maturity of adaptive signal processing technology, signal interference caused during electronic communication signal processing has become increasingly prominent. Therefore, this paper proposes to use the analysis of signal periodic characteristics and interference characteristics as variables, and modify the update relationship of step length in traditional algorithms. This enables the improved adaptive filtering algorithm to maintain its fast adaptive characteristics and excellent adaptive interference performance even under low signal-to-noise ratio conditions, achieving good experimental results. It can effectively extract changes in the periodic characteristics of communication signals. The application of the variable step-size least-squares algorithm for tracking time-varying signals is explored, specifically by introducing changes in the adaptive forgetting factor during the update process. This allows the forgetting factor value to be more flexible and have a more appropriate numerical range, thereby further enhancing its interference suppression capability and numerical stability. Under the same signal-to-noise ratio conditions (5 dB), the convergence speed of the improved variable step-size least-squares algorithm increased by 67%, and the steady-state value decreased by 2.7 dB. Similarly, in the application scenario of improving the time-varying tracking of the recursive least squares algorithm, when the interference signal-to-noise ratio is 20 dB, the convergence time is only 17.8 ms. This paper analyzes how to effectively improve the signal processing capability and interference resistance performance in the electronic communication signal processing process, thereby enhancing signal processing quality and effectiveness.