MATLAB Simulation of Linear Frequency Modulated Signals

This article discusses MATLAB simulation techniques for linear frequency modulated (LFM) signals and introduces methods for matched filtering and pulse compression processing. LFM signals are widely used in communication and radar systems. Through MATLAB simulation, we can better understand the characteristics of LFM signals and how to process them effectively. Matched filtering and pulse compression are standard techniques for processing LFM signals that help improve signal quality and detection accuracy. The implementation typically involves generating LFM signals using MATLAB's chirp function or custom algorithms that linearly vary frequency over time. For matched filtering, we create a time-reversed conjugated version of the transmitted signal as the filter impulse response, implemented using MATLAB's correlation functions or convolution operations. Pulse compression is achieved by applying the matched filter to the received signal, which compresses the energy into a narrow peak while suppressing noise. This article provides detailed explanations of these processing methods' principles and implementation steps, along with sample MATLAB code demonstrating signal generation using parameters like start/end frequencies and duration, filter design using correlation techniques, and compression result analysis through peak detection algorithms. By studying this material, readers will gain comprehensive understanding of LFM signal simulation and processing, enabling them to apply these techniques to solve practical engineering problems.