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
4312-4325
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
9 July 2021
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), .
(doi:10.1109/TSP.2021.3096044).
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.
This record has no associated files available for download.
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
Catalogue record
Date deposited: 20 Mar 2023 17:43
Last modified: 17 Mar 2024 13:11
Export record
Altmetrics
Contributors
Author:
Daniel E. Clark
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics