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Approximate Unconditional Maximum Likelihood Direction of Arrival Estimation for Two Closely Spaced Targets

Abstract : We consider Direction of Arrival (DoA) estimation in the case of two closely spaced sources. In this case, most high resolution techniques fail to estimate the two DoAs if the waveforms are highly correlated. Maximum Likelihood Estimators (MLE) are known to be more robust, but their excessive computational load limits their use in practice. In this paper, we propose an asymptotic approximation of the Unconditional Maximum Likelihood (UML) procedure in the case of a Uniform Linear Array (ULA) and two closely spaced targets. This approximation is based on an asymptotically (in the number of observations) equivalent formulation of the UML criterion, and on its Taylor series approximation for small DoA separation. This simplified procedure, which requires solving a 1D-optimization problem only, is shown to be accurate for source separation lower than half the mainlobe. Furthermore, it outperforms conventional high resolution algorithms in the case of two correlated sources.
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François Vincent, Olivier Besson, Eric Chaumette. Approximate Unconditional Maximum Likelihood Direction of Arrival Estimation for Two Closely Spaced Targets. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2014, vol. 22, pp. 86-89. ⟨10.1109/LSP.2014.2348011⟩. ⟨hal-01059131⟩

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