Provider: ingentaconnect
Database: ingentaconnect
Content: application/x-research-info-systems
TY - ABST
AU - Zhao, Lingling
AU - Ma, Peijun
AU - Su, Xiaohong
TI - An Improved Peak Extraction Algorithm for Probability Hypothesis Density Particle Filter
JO - Advanced Science Letters
PY - 2012-03-15T00:00:00///
VL - 6
IS - 1
SP - 88
EP - 95
KW - MULTI-TARGET TRACKING
KW - PEAK EXTRACTION
KW - PARTICLE FILTER
KW - PROBABILITY HYPOTHESIS DENSITY (PHD)
N2 - The Probability hypothesis density (PHD) particle filter is a new practical method to solve the unknown timevarying multi-target tracking problem. Peak extraction method is needed to detect the target states from the posterior PHD approximated by the particles and their weights. Tobias'
peak extraction algorithm sequentially removes the PHD component of each target, which works more efficiently than the *k*-means clustering and expectation-maximum algorithm. However, it becomes unreliable in dense targets environment. This paper improves Tobias' peak extraction method
in finding the particles representing a target and removing the effect of the target peaks. The proposed algorithm exploits a layer labeled technique to find the PHD component for a single target and segment the weights of these particles according to their corresponding distances to the candidate
of the target state. Demonstrations show that the proposed algorithm is more accurate and efficient compared with current-used peak extraction algorithms.
UR - http://www.ingentaconnect.com/content/asp/asl/2012/00000006/00000001/art00012
M3 - doi:10.1166/asl.2012.2014
UR - http://dx.doi.org/10.1166/asl.2012.2014
ER -