Abstract: Improving data association technique in dense clutter environment for multi-target tracking used in Markov chain Monte Carlo based particle filter (MCMC-PF) are discussed in this paper. A new method named Viterbi filtered gate Markov chain Monte Carlo VFG-MCMC is introduced to avoid track swap and to overcome the issue of loosing track to highly maneuvering targets in the presence of more background clutter and false signals. An adaptive search based on Viterbi algorithm is then used to detect the valid filtered data point in each target gate. The detected valid point for each target is applied to the estimation algorithm of MCMC-PF during calculating the sampling weights. This proposed method makes the MCMC interacts only with the valid target that is candidate from the filtered gate and no more calculations are considered for invalid targets. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm MCMC-PF.

E M Saad, El. Bardawiny, H.I.ALI and N.M.Shawky, “MCMC Particle Filter Using New Data Association Technique with Viterbi Filtered Gate Method for Multi-Target Tracking in Heavy Clutter ” International Journal of Advanced Computer Science and Applications(IJACSA), 2(8), 2011. http://dx.doi.org/10.14569/IJACSA.2011.020801

@article{Saad2011,
title = {MCMC Particle Filter Using New Data Association Technique with Viterbi Filtered Gate Method for Multi-Target Tracking in Heavy Clutter },
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.020801},
url = {http://dx.doi.org/10.14569/IJACSA.2011.020801},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {8},
author = {E M Saad and El. Bardawiny and H.I.ALI and N.M.Shawky}
}