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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.


Intensity functions for generic PHD filter (left) and AI-PHD filter (right).
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f5-sensors-12-02920: Intensity functions for generic PHD filter (left) and AI-PHD filter (right).

Mentions: We see that for different given and d, the results from two filters are close with the maximum difference being no more than 1 s. Figure 4(a) shows that in the scenario with low clutter density, AI-PHD filter has only a minor increase in average computation time over the generic PHD counterpart. Furthermore, AI-PHD filter performs an even low value in high clutter density scenario which is shown in Figure 4(b). In the case of , d = 8 where this reduction is most obvious, the average computation time of AI-PHD filter is reduced by 53.7% over the generic PHD counterpart, which means the AI-PHD filter has even lower computational complexity than the PHD filter without amplitude information in scenarios with dense clutter and high SNR. The primary reason for this trend is that in these scenarios, the computation time cost is mainly decided by the multi-target state extraction step given the same number of targets and measurements. Incorporated with amplitude information, the update for the AI-PHD filter (see Equation (33)) gives heavier weights to the Gaussian items updated by the measurements from targets, thus updating the PHD with comparatively higher intensity near the real target positions and at the same time, suppressing the intensity of PHD near clutter positions (see Figure 5). Therefore, the updated Gaussian items can be prune and merged quickly and accurately.


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)

Intensity functions for generic PHD filter (left) and AI-PHD filter (right).
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-12-02920: Intensity functions for generic PHD filter (left) and AI-PHD filter (right).
Mentions: We see that for different given and d, the results from two filters are close with the maximum difference being no more than 1 s. Figure 4(a) shows that in the scenario with low clutter density, AI-PHD filter has only a minor increase in average computation time over the generic PHD counterpart. Furthermore, AI-PHD filter performs an even low value in high clutter density scenario which is shown in Figure 4(b). In the case of , d = 8 where this reduction is most obvious, the average computation time of AI-PHD filter is reduced by 53.7% over the generic PHD counterpart, which means the AI-PHD filter has even lower computational complexity than the PHD filter without amplitude information in scenarios with dense clutter and high SNR. The primary reason for this trend is that in these scenarios, the computation time cost is mainly decided by the multi-target state extraction step given the same number of targets and measurements. Incorporated with amplitude information, the update for the AI-PHD filter (see Equation (33)) gives heavier weights to the Gaussian items updated by the measurements from targets, thus updating the PHD with comparatively higher intensity near the real target positions and at the same time, suppressing the intensity of PHD near clutter positions (see Figure 5). Therefore, the updated Gaussian items can be prune and merged quickly and accurately.

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.