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A bias-compensated MUSIC for small number of samples

Abstract : The multiple signal classification (MUSIC) method is known to be asymptotically efficient, yet with a small number of snapshots its performance degrades due to bias in MUSIC localization function. In this communication, starting from G-MUSIC which improves over MUSIC in low sample support, a high signal to noise ratio approximation of the G-MUSIC localization function is derived, which can be interpreted as a bias correction of the conventional MUSIC localization function. The resulting method, referred to as sG-MUSIC, is somewhat simpler than G-MUSIC as the weights applied to each eigenvector of the sample covariance matrix are obtained in closed-form, similarly to MUSIC. Numerical simulations indicate that sG-MUSIC incur only a marginal loss in terms of mean square error of the direction of arrival estimates, as compared to G-MUSIC, and performs better than MUSIC.
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François Vincent, Frédéric Pascal, Olivier Besson. A bias-compensated MUSIC for small number of samples. Signal Processing, Elsevier, 2017, 138, pp.117-120. ⟨10.1016/j.sigpro.2017.03.015⟩. ⟨hal-02486066⟩



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