Detection and Estimation of Multiple DoA Targets with Single Snapshot\n Measurements

2016/09/02 by Rakshith Jagannath, Jagannath, Rakshith
Computer Science · Engineering · Mathematics · #Applications (stat.AP) #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Probability (math.PR) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1609.00677

openalex publication_date 2016/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we explore the problems of detecting the number of\nnarrow-band, far-field targets and estimating their corresponding directions of\narrivals (DoAs) from single snapshot measurements. We use the principles of\nsparse signal recovery (SSR) for detection and estimation of multiple targets.\nIn the SSR framework, the DoA estimation problem is grid based and can be posed\nas the lasso optimization problem. The corresponding DoA detection problem\nreduces to estimating the optimal regularization parameter (\τ) of the\nlasso problem for achieving the required probability of correct detection\n(P_c). We propose finite sample and asymptotic test statistics for detecting\nthe number of sources with the required P_c at moderate to high signal to\nnoise ratios. Once the number of sources are detected, or equivalently the\noptimal \\τ is estimated, the corresponding DoAs can be estimated by\nsolving the lasso with regularization parameter set to \\τ.\n

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