An efficient multiple source localization approach: dynamic programming
Yi-Syun Huang, Mourad Barkat · 1990
A dynamic programming (DP) algorithm based on Bellman's principle of optimality (1962) for computing the MLE (maximum likelihood estimation) is proposed. By this algorithm, the multidimensional ML maximization problem is transformed into a recursively one-dimensional maximization problem. The global optimum of the MLE is guaranteed and obtained by simply maximizing the recursive likelihood function. The performance of the DP-MLE is compared to that of MUSIC via simulation. Three sources in the far field emitting plane waves into a linear sensor array of five elements uniformly half a wavelength apart are considered. It is shown that the noise performance of the DP-MLE is superior to that of MUSIC. This superiority is pronounced at low signal-to-noise ratios. It is concluded that the DP approach is an efficient algorithm for computing the MLEs of a number of sources and their directions of arrival.>