ESPRIT Algorithm Simulation for Spectral Estimation

In this article, we will examine the details and implementation of the ESPRIT algorithm through comprehensive simulation. By delving into the algorithm's execution process with practical code demonstrations, you will gain deeper insights into spectral estimation concepts and develop a more comprehensive understanding of ESPRIT. The implementation typically involves key steps such as signal subspace estimation through eigenvalue decomposition, rotational invariance property exploitation, and frequency parameter extraction using matrix pencil methods. We will also explore ESPRIT's application domains in array signal processing, wireless communications, and radar systems, while analyzing its advantages (such as computational efficiency and high resolution) and limitations (including sensitivity to model errors and noise conditions). Through this structured learning approach, you will be equipped to implement ESPRIT algorithm in practical scenarios using programming frameworks like MATLAB or Python with NumPy/SciPy libraries.