MATLAB Simulation Program for Pulse Compression Implementation

We can utilize Fast Fourier Transform (FFT) to convert time-domain signals into the frequency domain for pulse compression implementation. For this task, we can develop a MATLAB simulation program that employs built-in functions like fft() and ifft() for efficient domain transformations. The program typically involves generating a transmit signal (often a linear frequency modulated waveform), applying FFT to convert it to frequency domain, performing matched filtering with the complex conjugate of the reference signal, and finally applying inverse FFT to obtain the compressed pulse. Through this simulation, we can model the entire process and conduct performance analysis and optimization. We can experiment with different parameters such as signal sampling rates (controlled via MATLAB's sampling frequency variables) or filter types (using various window functions like hamming or chebyshev) to understand their impact on pulse compression performance metrics like sidelobe levels and mainlobe width. Furthermore, we can explore applications of this technique in other domains such as radar systems (range resolution improvement) or wireless communications (signal detection enhancement). Through continuous improvement and expansion of the simulation code, we can gain deeper insights into this field and establish foundations for future research.