A batch algorithm for estimating trajectories of point targets using expectation maximization
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
A batch algorithm for estimating trajectories of point targets using expectation maximization. / Rahmathullah, Abu ; Raghavendra, Selvan; Svensson, Lennart.
In: IEEE Transactions on Signal Processing, Vol. 64, No. 18, 2016, p. 4792-4804.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - A batch algorithm for estimating trajectories of point targets using expectation maximization
AU - Rahmathullah, Abu
AU - Raghavendra, Selvan
AU - Svensson, Lennart
PY - 2016
Y1 - 2016
N2 - In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT), but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement and a measurement is generated by at most one target. With this model assumption, we show that the proposed algorithm can be implemented as iterations of Rauch-Tung-Striebel (RTS) smoothing for state estimation, and the loopy belief propagation method for marginal data association probabilities calculation. Using example illustrations with tracks, we compare the proposed algorithm with PMHT and joint probabilistic data association (JPDA) and show that PMHT and JPDA exhibit coalescence when there are closely moving targets whereas the proposed algorithm does not. Furthermore, extensive simulations comparing the mean optimal sub-pattern assignment (MOSPA) performance of the algorithm for different scenarios averaged over several Monte Carlo iterations show that the proposed algorithm performs better than JPDA and PMHT. We also compare it to benchmarking algorithm: N- scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good trade-off between computational complexity and the MOSPA performance.
AB - In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT), but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement and a measurement is generated by at most one target. With this model assumption, we show that the proposed algorithm can be implemented as iterations of Rauch-Tung-Striebel (RTS) smoothing for state estimation, and the loopy belief propagation method for marginal data association probabilities calculation. Using example illustrations with tracks, we compare the proposed algorithm with PMHT and joint probabilistic data association (JPDA) and show that PMHT and JPDA exhibit coalescence when there are closely moving targets whereas the proposed algorithm does not. Furthermore, extensive simulations comparing the mean optimal sub-pattern assignment (MOSPA) performance of the algorithm for different scenarios averaged over several Monte Carlo iterations show that the proposed algorithm performs better than JPDA and PMHT. We also compare it to benchmarking algorithm: N- scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good trade-off between computational complexity and the MOSPA performance.
U2 - 10.1109/TSP.2016.2572048
DO - 10.1109/TSP.2016.2572048
M3 - Journal article
VL - 64
SP - 4792
EP - 4804
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 18
ER -
ID: 161848912