MATLAB Simulation Program for Pulse Compression Implementation

Pulse compression is a key technology in radar signal processing, primarily used to improve radar range resolution and signal-to-noise ratio (SNR). It works by transmitting wide-pulse signals and performing compression processing on the received echo signals at the receiver end, effectively achieving narrow-pulse performance. Frequency-domain pulse compression implementation typically utilizes Fast Fourier Transform (FFT) to enhance computational efficiency. In MATLAB simulation, the fundamental approach for frequency-domain pulse compression implementation involves the following steps: Signal Generation A wideband signal needs to be generated first, with Linear Frequency Modulation (LFM) signals being commonly used waveforms. By adjusting the frequency modulation bandwidth and pulse width, the resolution of the compressed signal can be controlled. In MATLAB implementation, this can be achieved using the `chirp` function with parameters specifying start/end frequencies and time duration. Matched Filter Design The matched filter is designed to maximize output SNR. In the frequency domain, this filter's frequency spectrum is typically the conjugate spectrum of the transmitted signal. MATLAB implementation involves calculating the conjugate of the transmitted signal's FFT using the `conj` function applied to the FFT result. Frequency Domain Convolution and FFT Processing The received signal and matched filter's frequency spectrum are multiplied (frequency domain convolution is equivalent to time domain correlation operation), followed by Inverse Fast Fourier Transform (IFFT) to convert back to the time domain, yielding the compressed pulse signal. This is implemented in MATLAB using `fft` for forward transform, element-wise multiplication with `.*` operator, and `ifft` for the inverse transformation. Result Analysis The compressed time-domain waveform is examined to analyze main lobe width, sidelobe levels, and SNR improvement, thereby evaluating the pulse compression effectiveness. MATLAB provides functions like `plot` for waveform visualization and `max`, `find` functions for parameter measurements. This method finds widespread applications in radar, sonar, and other fields, effectively enhancing system detection performance while significantly reducing computational complexity through frequency-domain processing. The implementation typically involves proper zero-padding using `zeros` function before FFT operations to avoid circular convolution effects and ensure accurate linear convolution results.