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Optimization Strategies for Bayesian Source Localization Algorithms
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AI
DOI：10.1109/TASE.2022.3154228.png)
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Abstract
En 中文
Target search via Bayesian estimation is a commonly studied problem in which the goal is to locate and possibly characterize a lost ``target'' within a given space using noisy sensors. Example applications include locating gas leaks, radio sources, ships at sea, etc, using appropriate sensors for the specific phenomena. The motivating application for this effort is detecting and locating nearby radioactive material in nuclear facilities. Past work has produced several implementations for addressing this problem, with varying levels of accuracy and sophistication. Here we present both new and previously developed solution methods to the radioactive source localization problem. The solutions have diverse strategies for selecting advantageous positions at which to collect measurements. The objective is to minimize the number of measurements needed to complete the target localization. We empirically compare the effectiveness of various strategies, three of which are novel and prove superior over prior methods of measurement selection. The relative advantages of the novel strategies are discussed as well as their broader applicability to a general class of target search problems where measurement intensity correlates with target proximity.
Keywords:
Particle measurements
Atmospheric measurements
Location awareness
Position measurement
Optimization
Detectors
Bayes methods
Robotics
gamma radiation
surveying
Bayesian estimation
optimization
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