NDLI: Multi-bernoulli sensor control for multi-target tracking

Content Provider	IEEE Xplore Digital Library
Author	Gostar, A.K. Hoseinnezhad, R. Bab-Hadiashar, A.
Copyright Year	2013
Description	Author affiliation: Sch. of Aerosp., Mech. & Manuf. Eng., RMIT Univ., Melbourne, VIC, Australia (Gostar, A.K.; Hoseinnezhad, R.; Bab-Hadiashar, A.)
Abstract	A new approach to solve the sensor control problem is proposed, formulated based on multi-object Bayes filtering in the partially observable Markov decision process (POMDP) context, where the multi-object states are assumed to be random finite sets with multi-Bernoulli distributions. We introduce a novel cost function that is reliable in real-time environment. In each filtering iteration, after predicting the multi-Bernoulli parameters, estimates for the number and states of the targets are extracted. For each admissible control command, Monte-Carlo samples of measurements corresponding to the estimated target states are generated. Then, for each measurement sample, the CB-MeMBer update is performed and the average cost function is computed. The best command is the one incurring the minimum cost. The simulation results involve a challenging case of detecting and tracking up to 5 manoeuvring targets using a controllable sensor, and show that our method outperforms competing methods both in terms of tracking accuracy (measured in using OSPA metric) and in terms of computational cost.
Starting Page	312
Ending Page	317
File Size	365738
Page Count	6
File Format	PDF
ISBN	9781467354998
e-ISBN	9781467355018
DOI	10.1109/ISSNIP.2013.6529808
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-02
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Monte Carlo methods Estimation Probability Cost function Approximation methods
Content Type	Text
Resource Type	Article

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