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Minimum Mean-Squared-Error Autocorrelation Processing in Coprime Arrays

[Submitted on 21 Oct 2020]

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Abstract: Coprime arrays enable Direction-of-Arrival (DoA) estimation of an increased
number of sources. To that end, the receiver estimates the autocorrelation
matrix of a larger virtual uniform linear array (coarray), by applying
selection or averaging to the physical array’s autocorrelation estimates,
followed by spatial-smoothing. Both selection and averaging have been designed
under no optimality criterion and attain arbitrary (suboptimal)
Mean-Squared-Error (MSE) estimation performance. In this work, we design a
novel coprime array receiver that estimates the coarray autocorrelations with
Minimum-MSE (MMSE), for any probability distribution of the source DoAs. Our
extensive numerical evaluation illustrates that the proposed MMSE approach
returns superior autocorrelation estimates which, in turn, enable higher DoA
estimation performance compared to standard counterparts.

Submission history

From: Dimitris Chachlakis [view email]

Wed, 21 Oct 2020 15:26:48 UTC (436 KB)

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