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FISST based method for multi-target tracking in the image plane of optical sensors.

Xu Y, Xu H, An W, Xu D - Sensors (Basel) (2012)

Bottom Line: Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given.Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter.Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. xuyang012@nudt.edu.cn

ABSTRACT
A finite set statistics (FISST)-based method is proposed for multi-target tracking in the image plane of optical sensors. The method involves using signal amplitude information in probability hypothesis density (PHD) filter which is derived from FISST to improve multi-target tracking performance. The amplitude of signals generated by the optical sensor is modeled first, from which the amplitude likelihood ratio between target and clutter is derived. An alternative approach is adopted for the situations where the signal noise ratio (SNR) of target is unknown. Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given. Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter. Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter.

No MeSH data available.


Target trajectories in the pixel plane with start/stop position as O/▵.
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f1-sensors-12-02920: Target trajectories in the pixel plane with start/stop position as O/▵.

Mentions: Consider a scenario with an unknown and time varying number of targets in clutter in the image region [−300, 300] × [2,000, 2,600] (pixel). Up to Nk = 6 targets are generated in this region with the random birth and dieing time instants. Figure 1 shows the true trajectories of each target. All targets in each simulation had the same mean SNR (this is not necessary by the algorithm but simplifies the presentation of results).


FISST based method for multi-target tracking in the image plane of optical sensors.

Xu Y, Xu H, An W, Xu D - Sensors (Basel) (2012)

Target trajectories in the pixel plane with start/stop position as O/▵.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376575&req=5

f1-sensors-12-02920: Target trajectories in the pixel plane with start/stop position as O/▵.
Mentions: Consider a scenario with an unknown and time varying number of targets in clutter in the image region [−300, 300] × [2,000, 2,600] (pixel). Up to Nk = 6 targets are generated in this region with the random birth and dieing time instants. Figure 1 shows the true trajectories of each target. All targets in each simulation had the same mean SNR (this is not necessary by the algorithm but simplifies the presentation of results).

Bottom Line: Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given.Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter.Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. xuyang012@nudt.edu.cn

ABSTRACT
A finite set statistics (FISST)-based method is proposed for multi-target tracking in the image plane of optical sensors. The method involves using signal amplitude information in probability hypothesis density (PHD) filter which is derived from FISST to improve multi-target tracking performance. The amplitude of signals generated by the optical sensor is modeled first, from which the amplitude likelihood ratio between target and clutter is derived. An alternative approach is adopted for the situations where the signal noise ratio (SNR) of target is unknown. Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given. Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter. Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter.

No MeSH data available.