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Importance Sampling-Based Maximum Likelihood Estimation for Multidimensional Harmonic Retrieval
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DOI：10.1109/LSP.2015.2498195.png)
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Abstract
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This letter addresses a maximum likelihood (ML) algorithm for multidimensional (m-D) harmonic retrieval (MHR) problems. The new algorithm iteratively estimates the parameters in a rough to fine manner, intervened with filtering processes to separate the signals into appropriate groups. To facilitate implementations of the ML estimation, a Monte Carlo method, importance sampling (IS), and the theorem of Pincus are utilized to determine the ML estimates. Moreover, the pairing of the estimated parameters is automatically achieved without extra 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) scenarios.
Keywords:
Filtering
importance sampling
maximum likelihood
multidimensional harmonic retrieval
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