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arXiv · 2610.38123

Covariance Learning for Direction-of-Arrival Estimation with Sparse Linear Arrays

Majdoddin Esfandiari·Esa Ollila·Geert Leus·Daniel P. Palomar
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Abstract

Covariance learning (CL) estimates source powers and sensor-noise variance by minimizing the Type-II (marginal) negative log-likelihood of the array measurements. We develop both grid-based and gridless CL direction-of-arrival (DOA) estimators applicable to arbitrary sparse linear array (SLA) geometries. The grid-based DOA estimator minimizes the Type-II objective on an angular grid via successive convex approximation (SCA) and estimates the noise variance by a scalar bisection search. The gridless DOA estimator then refines each grid-based DOA over the continuum using a Newton method. Simulations on a six-sensor nested array, including an underdetermined case with nine sources, show that the proposed DOA estimators outperform state-of-the-art methods, despite jointly estimating the sensor noise variance.

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Majdoddin Esfandiari, Esa Ollila, Geert Leus, Daniel P. Palomar. 2026-10-28. Covariance Learning for Direction-of-Arrival Estimation with Sparse Linear Arrays. https://arxiv.org/abs/2610.38123

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