[Submitted on 29 Apr 2026 (v1), last revised 22 Jul 2026 (this version, v2)]

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Abstract:We consider the problems of computing the optimal rank-1 Hankel and Toeplitz-structured approximation of arbitrary matrices under L2 and L1-norm error. Such problems arise naturally in engineered systems, including the basic few-shot signal Direction-of-Arrival (DoA) estimation problem that is of importance to modern autonomous systems applications. We develop accurate and computationally efficient structured matrix decomposition algorithms for both formulations and then derive analytically grounded small-sample-support DoA estimators for practical sensing system deployments. The resulting estimators under the L2 and L1 norms are formally shown to be maximum-likelihood optimal under white Gaussian and Laplace noise, respectively. The estimators are further validated through extensive simulation studies and real-world data experiments in few-shot DoA inference.

Submission history

From: George Sklivanitis [view email]
[v1] Wed, 29 Apr 2026 15:19:26 UTC (526 KB) (withdrawn)
[v2] Wed, 22 Jul 2026 19:49:59 UTC (525 KB)