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Field estimation using binary measurements
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AI
DOI：10.1016/j.sigpro.2021.108430.png)
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Abstract
En 中文
In this paper we consider the problem of field estimation using binary sensor measurements. A number of agents are equipped with sensors which can make binary measurements of the field. The agents are mobile and can move around to different locations to collect measurements. In estimating the field, we model the field as a sum of radial basis functions, whose parameters are then estimated using sequential Monte Carlo techniques. We incorporate an active sensing mechanism for the agents to adaptively choose their next measurement location, given the information currently collected. In the multi-agent case, we consider both a centralized setup, and a decentralized setup where agents share their past measurements with each other provided they are within communication range. Simulation studies illustrate the performance of the proposed algorithms.Crown Copyright (c) 2021 Published by Elsevier B.V. All rights reserved.
Keywords:
Autonomous agents
Field estimation
Particle filtering
Quantized measurements
Sensor management