An information-based approach to sensor management in large dynamic networks
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2007-01-01
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Proceedings of the IEEE
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Melbourne Authors
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Electrical and Electronic Engineering
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Journal Article
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Kreucher, C. M., Hero, A. O., Kastella, K. D. & Morelande, M. R. (2007). An information-based approach to sensor management in large dynamic networks. Proceedings of the IEEE, 95 (5), pp.978-999. https://doi.org/10.1109/JPROC.2007.893247.
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Abstract
This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management, as defined here, is the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility. Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low-communication environments, decentralized methods are required. The approach we present in this paper addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are detected and tracked by a network of mobile sensors.
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Artificial Intelligence and Image Processing