An Improved Algorithm for Basic ESPRIT

This program implements an improved version of the basic ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm for modern signal processing applications. The enhanced algorithm incorporates advanced matrix decomposition techniques and signal subspace identification methods to achieve better frequency estimation accuracy. Using this algorithm, we conducted statistical simulation experiments for signal frequency analysis. The simulation employs numerical computation approaches including covariance matrix calculation, eigenvalue decomposition, and signal subspace extraction to process synthetic test signals. Through these experiments, which involve signal generation, noise addition, and parameter estimation routines, we can obtain valuable statistical results regarding signal frequency characteristics. The implementation demonstrates practical applications of array processing techniques and provides insights into signal processing principles and methodologies. These findings are crucial for further research and practical applications in signal processing technology, particularly in areas requiring high-resolution frequency estimation. The code structure includes modules for signal simulation, algorithm core functions, and result visualization, allowing for comprehensive performance evaluation of the improved ESPRIT method.