Notes on the tightness of the hybrid Cramér-Rao lower bound
IEEE Transactions on Signal Processing
Maximum likelihood time-of-arrival estimation of optical pulses via photon-counting photodetectors
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
IEEE Transactions on Signal Processing
Noncoherent MIMO radar for location and velocity estimation: more antennas means better performance
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Efficient Spectrum Allocation and Time of Arrival Based Localization in Cognitive Networks
Wireless Personal Communications: An International Journal
Range Estimation in a Time Varying Multipath Channel
Wireless Personal Communications: An International Journal
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This paper presents a performance analysis of the maximum likelihood (ML) estimator for finding the directions of arrival (DOAs) with a sensor array. The asymptotic properties of this estimator are well known. In this paper, the performance under conditions of low signal-to-noise ratio (SNR) and a small number of array snapshots is investigated. It is well known that the ML estimator exhibits a threshold effect, i.e., a rapid deterioration of estimation accuracy below a certain SNR or number of snapshots. This effect is caused by outliers and is not captured by standard techniques such as the Crame´r-Rao bound and asymptotic analysis. In this paper, approximations to the mean square estimation error and probability of outlier are derived that can be used to predict the threshold region performance of the ML estimator with high accuracy. Both the deterministic ML and stochastic ML estimators are treated for the single-source and multisource estimation problems. These approximations alleviate the need for time-consuming computer simulations when evaluating the threshold region performance. For the special case of a single stochastic source signal and a single snapshot, it is shown that the ML estimator is not statistically efficient as SNR→∞ due to the effect of outliers.