Abstract
Maximum Likelihood Estimator (MLE) has been shown to be the best performance in parameter estimation. However, the computation burden of MLE is very large. In order to resolve the question of computation burden, Monte Carlo methods are combined with maximum likelihood DOA estimator. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only keeps the excellent performance of the original MLE, but also reduces the computation greatly, from the computational complexity O(LK) of original method to O(K×H).
Original language | English |
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Pages (from-to) | 1529-1532 |
Number of pages | 4 |
Journal | Tien Tzu Hsueh Pao/Acta Electronica Sinica |
Volume | 33 |
Issue number | 8 |
State | Published - Aug 2005 |
Keywords
- Computational complexity
- DOA estimation
- Importance sampling
- Maximum likelihood estimator
- Monte Carlo