PROJECT TITLE :

Importance Sampling-Based Maximum Likelihood Estimation for Multidimensional Harmonic Retrieval

ABSTRACT:

This letter addresses a most probability (ML) algorithm for multidimensional (m-D) harmonic retrieval (MHR) issues. The new algorithm iteratively estimates the parameters in a rough to fine manner, intervened with filtering processes to separate the signals into applicable groups. To facilitate implementations of the ML estimation, a Monte Carlo methodology, importance sampling (IS), and the theory of Pincus are utilised to work out the ML estimates. Moreover, the pairing of the estimated parameters is automatically achieved without additional overhead. Conducted simulations demonstrate that the new algorithm outperforms the main state-of-the-art works and can achieve the Cramer-Rao lower bound (CRLB) even in low signal-to-noise ratio (SNR) eventualities.