Maximum Likelihood Estimator (MLE) has been shown to be the best performance in parameter estimation.However

the computation burden of MLE is very lagre.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(L

K

)

of original method to

O(K×H)

.