@inproceedings{37749ce20b7b4230ac814f3cfec5c1c7,
title = "Scheduling multiple sensors using particle filters in target tracking",
abstract = "A critical component of a multi-sensor system is sensor scheduling to optimize system performance under constraints (e.g. power, bandwidth, and computation). In this paper, we apply particle filter sequential Monte Carlo methods to implement multiple sensor scheduling for target tracking. Under the constraint that only one sensor can be used at each time step, we select a sequence of sensor uses to minimize the predicted mean-square error in the target state estimate; the predicted mean-square error is approximated using the particle filter in conjunction with an extended Kaiman filter approximation. Using Monte Carlo simulations, we demonstrate the improved performance of our scheduling approach over the non-scheduling case.",
keywords = "Cost function, Infrared sensors, Particle filters, Particle measurements, Processor scheduling, Radar measurements, Radar tracking, Sensor systems, State estimation, Target tracking",
author = "Chhetri, {A. S.} and Darryl Morrell and Antonia Papandreou-Suppappola",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; IEEE Workshop on Statistical Signal Processing, SSP 2003 ; Conference date: 28-09-2003 Through 01-10-2003",
year = "2003",
doi = "10.1109/SSP.2003.1289522",
language = "English (US)",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
pages = "549--552",
booktitle = "Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003",
}