Importance Sampling-Based Maximum Likelihood Estimation for Multidimensional Harmonic Retrieval PROJECT TITLE :Importance Sampling-Based Maximum Likelihood Estimation for Multidimensional Harmonic RetrievalABSTRACT: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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest A Survey of Public-Key Cryptographic Primitives in Wireless Sensor Networks Energy-Minimized Design and Operation of IP Over WDM Networks With Traffic-Aware Adaptive Router Card Clock Frequency
