Approximate stochastic dynamic programming for sensor scheduling to track multiple targets
- 31 December 2009
- journal article
- Published by Elsevier BV in Digital Signal Processing
- Vol. 19 (6), 978-989
- https://doi.org/10.1016/j.dsp.2007.05.004
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Pomdp Approximation Using Simulation and HeuristicsPublished by Springer Science and Business Media LLC ,2008
- Dynamic Sensor Management for Multisensor Multitarget TrackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Sensor scheduling for target tracking: A Monte Carlo sampling approachDigital Signal Processing, 2005
- Particle filtering for multitarget detection and trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Real-Time Particle FiltersProceedings of the IEEE, 2004
- A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Transactions on Signal Processing, 2002
- Algorithms for optimal scheduling and management of hidden Markov model sensorsIEEE Transactions on Signal Processing, 2002
- Monte Carlo data association for multiple target trackingPublished by Institution of Engineering and Technology (IET) ,2001
- Optimal sensor scheduling for hidden Markov model state estimationInternational Journal of Control, 2001
- Optimal sensor selection strategy for discrete-time state estimatorsIEEE Transactions on Aerospace and Electronic Systems, 1994