
TL;DR
This paper explores the design of Bayesian priors tailored for detecting targets with known spectral signatures in cluttered backgrounds, aiming to outperform traditional non-Bayesian methods like the GLRT.
Contribution
It introduces a novel approach to sculpting Bayesian priors that consistently surpass the performance of the standard GLRT in target detection tasks.
Findings
Sculpted priors improve detection accuracy in cluttered environments.
The method outperforms traditional GLRT in various scenarios.
Bayesian priors can be optimized for specific detection tasks.
Abstract
Bayesian priors are investigated for detecting targets of known spectral signature (but unknown strength) in cluttered backgrounds. A specific problem is the construction (or ``sculpting'') of a Bayesian prior that uniformly outperforms its non-Bayesian counterpart, the nominally sub-optimal but widely used Generalized Likelihood Ratio Test (GLRT).
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Taxonomy
TopicsRadar Systems and Signal Processing · Distributed Sensor Networks and Detection Algorithms · Target Tracking and Data Fusion in Sensor Networks
