Signal Parameter Estimation Using Rotational Invariance Techniques

The methodology implemented in this code employs the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), which has evolved into a primary eigenvalue decomposition approach for harmonic recovery - specifically identifying the number of sinusoidal waves and estimating their frequencies. The algorithm works by constructing a Hankel matrix from the input signal and performing subspace decomposition to exploit the rotational invariance property between signal subspaces.

New paragraph: The Rotational Invariance Technique is a signal parameter estimation method based on eigenvalue decomposition that accurately determines the number of sinusoidal components and their frequencies. This approach is widely utilized in harmonic recovery applications and has become a fundamental analytical tool. Using this code implementation, you can efficiently apply the ESPRIT algorithm to estimate signal parameters, enabling precise signal analysis and reconstruction. The core functionality includes automatic signal subspace identification, frequency estimation through eigenvalue computation, and amplitude/p phase recovery using least-squares fitting.