Chirp Signal Matched Filtering: Spectral Analysis and Simulation Implementation

This paper conducts a detailed spectral analysis of chirp signals and demonstrates the performance of chirp signal matched filtering through simulation experiments. During the analysis process, we thoroughly examine the characteristics of chirp signals along with the principles and applications of matched filtering. By analyzing the chirp signal's spectrum, we gain better understanding of its frequency characteristics and utilize matched filtering to extract desired signal information. The simulation results indicate that chirp signal matched filtering effectively extracts target signals while demonstrating strong anti-interference capabilities. From an implementation perspective, the chirp signal generation typically involves creating a linearly or exponentially frequency-modulated signal using mathematical functions like chirp() in MATLAB or similar libraries in Python. The matched filter implementation involves creating a time-reversed complex conjugate of the known chirp template, which is then convolved with the received signal. Key algorithms include Fast Fourier Transform (FFT) for efficient frequency domain processing and correlation techniques for optimal signal detection. The simulation typically involves adding noise components to test the filter's robustness and calculating performance metrics like signal-to-noise ratio improvement. Therefore, chirp signal matched filtering shows broad application prospects in signal processing fields such as radar systems, sonar applications, and communication systems, where its pulse compression properties enable high-resolution detection and improved range resolution.