Performance Analysis of ESPRIT Algorithms

This literature systematically examines multiple ESPRIT algorithm variants and conducts comprehensive performance analysis. ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) is a fundamental algorithm for signal parameter estimation with extensive applications across communication systems, radar imaging, and biomedical engineering. The algorithm typically operates by exploiting the rotational invariance property in signal subspaces, often implemented through eigenvalue decomposition of covariance matrices or singular value decomposition of data matrices. Beyond performance evaluation, this work provides detailed explanations of ESPRIT's underlying principles and practical application examples, demonstrating key implementation aspects such as array geometry requirements, subspace pairing techniques, and parameter resolution thresholds. The included MATLAB/Python code snippets illustrate critical steps including signal subspace estimation, eigenvalue decomposition using functions like numpy.linalg.eig(), and parameter extraction through phase estimation. This research serves as an essential reference for professionals and researchers interested in signal processing and parameter estimation techniques, offering both theoretical insights and practical implementation guidelines.