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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.


Filter estimates with known SNR.
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f2-sensors-12-02920: Filter estimates with known SNR.

Mentions: The effectiveness of our AI-PHD filter for multi-target tracking in image plane of optical sensor is verified through simulation. We assume a moderately cluttered scenario that the probabilities of false alarm, , which means the clutter density, λ = 1 × 10−4pixel−2. The SNRs of all targets are set as d = 6 and the probability of detection (see Table 1). For the unknown SNR case, the SNR region is set as [2,10] and the probability of detection is replaced by which can be computed by Equation (12). Other parameters for the filter are given as in Section 4.1. The true trajectories and filter estimates are shown in x and y coordinates of image plane versus time for AI-PHD filter with known and unknown target SNRs in Figures 2 and 3 respectively (denoted as case1 and case2 accordingly).


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)

Filter estimates with known SNR.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-12-02920: Filter estimates with known SNR.
Mentions: The effectiveness of our AI-PHD filter for multi-target tracking in image plane of optical sensor is verified through simulation. We assume a moderately cluttered scenario that the probabilities of false alarm, , which means the clutter density, λ = 1 × 10−4pixel−2. The SNRs of all targets are set as d = 6 and the probability of detection (see Table 1). For the unknown SNR case, the SNR region is set as [2,10] and the probability of detection is replaced by which can be computed by Equation (12). Other parameters for the filter are given as in Section 4.1. The true trajectories and filter estimates are shown in x and y coordinates of image plane versus time for AI-PHD filter with known and unknown target SNRs in Figures 2 and 3 respectively (denoted as case1 and case2 accordingly).

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.