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Multi-sensor network information for linear-gaussian multi-target tracking systems

Multi-sensor network information for linear-gaussian multi-target tracking systems
Multi-sensor network information for linear-gaussian multi-target tracking systems
Methods for information-theoretic control for networks of sensors are of interest for enabling the development of autonomous sensor systems. In this paper we revisit the fundamentals of information theoretic-based control for multi-target systems and present a systematic approach for determining information-theoretic situational awareness based on mutual information for point processes. The extension to multi-sensor systems is developed using the concept of a broadcast channel from information theory. Analytic results are presented for linear-Gaussian systems which enable low complexity solutions for determining information from multiple sensors and we consider a large number of potential sensor configurations. We consider extensions of single-target methods to multi-target scenarios and present results in simulations.
Multi-sensor control, Multi-target tracking, Mutual information, Network information theory, Stochastic control
1053-587X
4312-4325
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393

Clark, Daniel E. (2021) Multi-sensor network information for linear-gaussian multi-target tracking systems. IEEE Transactions on Signal Processing, 69 (7), 4312-4325. (doi:10.1109/TSP.2021.3096044).

Record type: Article

Abstract

Methods for information-theoretic control for networks of sensors are of interest for enabling the development of autonomous sensor systems. In this paper we revisit the fundamentals of information theoretic-based control for multi-target systems and present a systematic approach for determining information-theoretic situational awareness based on mutual information for point processes. The extension to multi-sensor systems is developed using the concept of a broadcast channel from information theory. Analytic results are presented for linear-Gaussian systems which enable low complexity solutions for determining information from multiple sensors and we consider a large number of potential sensor configurations. We consider extensions of single-target methods to multi-target scenarios and present results in simulations.

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More information

Published date: 9 July 2021
Additional Information: Funding Information: This work was supported by Joint AFRL-Dstl Basic-Research Grant inAutonomous Signal Processing(AFOSR under Grant FA9550-19-1-7008 and Dstl Task 1000133068). Funding Information: Manuscript received February 23, 2021; revised June 7, 2021 and June 25, 2021; accepted July 1, 2021. Date of publication July 9, 2021; date of current version August 16, 2021. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Bo Tang. This work was supported by Joint AFRL-Dstl Basic-Research Grant in Autonomous Signal Processing (AFOSR under Grant FA9550-19-1-7008 and Dstl Task 1000133068). (Corresponding author: Daniel Clark.) The author is with the Télécom SudParis, Institut Polytechnique Paris Mines Telecom, 91001 Évry, France (e-mail: daniel.clark@telecom-sudparis.eu). Digital Object Identifier 10.1109/TSP.2021.3096044 Publisher Copyright: © 1991-2012 IEEE.
Keywords: Multi-sensor control, Multi-target tracking, Mutual information, Network information theory, Stochastic control

Identifiers

Local EPrints ID: 475492
URI: http://eprints.soton.ac.uk/id/eprint/475492
ISSN: 1053-587X
PURE UUID: 8d565cb7-34a9-45ec-9ad9-7aa4cd00dbea

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Date deposited: 20 Mar 2023 17:43
Last modified: 17 Mar 2024 13:11

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Author: Daniel E. Clark

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