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Maximum Likelihood-Based Direction-of-Arrival Estimator for Discrete Sources

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Abstract

This paper addresses the problem of direction-of-arrival (DOA) parameter estimation in array processing when the signals are inherently discrete, which is the case mainly in the digital communication context. Based on the particular structure of the signal space in the data model, a maximum likelihood-based approach is introduced. The strategy consists in transforming the parameter estimation problem into a decision task. It is shown through numerical simulations that the proposed solution closely follows the performance limit given by the Cramér–Rao bound. Some important features of the technique are as follows: (i) it is capable of handling any number of sources, provided that the number of sensors is greater than or equal to two and the number of snapshots is sufficiently greater than the cardinality of the signal space; (ii) the estimation quality is not affected by the angle and phase separation; and (iii) it offers the possibility to deal with uncalibrated arrays.

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  1. Among the most relevant references we highlight [36] and [28].

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Acknowledgements

The authors thank FAPESP, CNRS, and CNPq for their financial support.

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Correspondence to Rafael Krummenauer.

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Krummenauer, R., Ferrari, R., Suyama, R. et al. Maximum Likelihood-Based Direction-of-Arrival Estimator for Discrete Sources. Circuits Syst Signal Process 32, 2423–2443 (2013). https://doi.org/10.1007/s00034-013-9583-6

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