Abstract
Emitter Direction-of-Arrival (DOA) estimation is a fundamental problem in a variety of applications including radar, sonar, and wireless communications. The research has received considerable attention in literature and numerous methods have been proposed. Maximum Likelihood (ML) is a nearly optimal technique producing superior estimates compared to other methods especially in unfavorable conditions, and thus is of significant practical interest. This paper discusses in details the techniques for ML DOA estimation in either white Gaussian noise or unknown noise environment. Their performances are analyzed and compared, and evaluated against the theoretical lower bounds.
Highlights
Estimation of the emitters’ directions with an antenna array, or Direction-of-Arrival (DOA) estimation, is an essential problem in a large variety of applications such as radar, sonar, mobile communications, and seismic exploration, because it is a major method for location determination
Antennas, and it is assumed that the thermal noise is dominant, a good model for the noise is white Gaussian noise with covariance being a Unconditional Maximum Likelihood (UML) estimator scaled identity matrix in (3), If we assume that both the signals and the noise are stationary, Q
2 n is the noise power, and I is an identity matrix. This model assumes that the noise intensity is the same in all sensors and temporally white, zero-mean complex Gaussian random processes with second-order moments satisfying (3) and (5), following a similar derivation procedure, we may conclude that the UML estimator is given by minimizing (15)
Summary
Estimation of the emitters’ directions with an antenna array, or Direction-of-Arrival (DOA) estimation, is an essential problem in a large variety of applications such as radar, sonar, mobile communications, and seismic exploration, because it is a major method for location determination. ML produces superior estimates compared to other methods, especially in unfavorable conditions involving low SNR, short data samples, highly correlated or coherent sources, and small array apertures, and is of practical interest. It can be used as a caliber to evaluate the performance of other methods. The computational burden of DOA estimation with large arrays is often prohibitively extensive To address this challenge, a robust solution for data reduction (and computation reduction) in array processing is presented in the study of Li and Lu [4,5].
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