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极大似然估计器是波达方向估计中公认的最佳估计器,但是计算量很大。为了解决极大似然估计器由于进行多维格形搜索而带来的计算量大的不足,将粒子滤波方法与极大似然估计相结合,提出了一种基于粒子滤波的极大似然波达方向估计器(Maximum Likelihood DOA Estimator Based on Particle Filtering,简称MLE-PF)。研究结果表明,MLE-PF不但保持了原极大似然估计方法的优良性能,大大减小了计算量,计算复杂度由O(LK)降至O(K×Ns),而且在低信噪比时也具有比MUSIC以及MiniNorm方法更加优越的估计性能。
Maximum likelihood estimator is the best estimator in DOA estimation, but it is computationally intensive. In order to solve the problem that maximum likelihood estimator has a large computational load due to multi-dimensional grid search, a particle filter-based maximum likelihood sequence Maximum Likelihood DOA Estimator Based on Particle Filtering (MLE-PF). The results show that MLE-PF not only retains the excellent performance of the original maximum likelihood estimation method, greatly reduces the computational complexity, and reduces the computational complexity from O (LK) to O (K × Ns) It also has more superior estimation performance than MUSIC and MiniNorm methods.