Tracking variable number of targets using sequential monte carlo methods
- 1 January 2005
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1286-1291
- https://doi.org/10.1109/ssp.2005.1628794
Abstract
In this paper, we present a simulation-based method for multitarget tracking and detection using sequential Monte Carlo (SMC), or particle filtering (PF) methods. The proposed approach is applicable to nonlinear and non-Gaussian models for the target dynamics and measurement likelihood, where the environment is characterised by high clutter rate and low detection probability. The number of targets is estimated by continuously monitoring the events being represented by the regions of interest (ROIs) in the surveillance region. Subsequent to target detection, the sequential importance sampling filter is employed for recursive target state estimation, in conjunction with a 2-D data assignment method for measurement-to-target association. Computer simulations are also included to demonstrate and evaluate the performance of the proposed approachKeywords
This publication has 2 references indexed in Scilit:
- A hybrid approach for online joint detection and tracking for multiple targetsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Multi-target particle filtering for the probability hypothesis densityPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003