MATLAB Simulation Experiment for Compressed Sensing Radar Signal Processing

In this experiment, we employ compressed sensing radar signal processing methods to model target scenes and process echo signals. Compressed sensing is an emerging signal processing technique that significantly reduces sampling rates while maintaining high accuracy, thereby decreasing system complexity and cost. Through MATLAB simulations, we will conduct in-depth exploration of compressed sensing principles and applications. The implementation will involve key algorithms such as sparse signal representation using orthogonal matching pursuit (OMP) or basis pursuit algorithms, measurement matrix design using random Gaussian matrices, and signal reconstruction through L1-norm optimization techniques. We will investigate the application of this technology in radar signal processing and examine how compressed sensing can be implemented in practical scenarios. The MATLAB code will demonstrate critical functions including signal sparsification, compressed measurements acquisition, and reconstruction error analysis. Through this experiment, we will gain comprehensive understanding of both advantages and limitations of compressed sensing radar signal processing, establishing solid foundation for future research work. The simulation will include performance comparisons between traditional and compressed sensing approaches under different signal-to-noise ratio conditions.