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.
Highlights
Optical sensors have been widely applied both in important military and civil areas due to their properties of long detection range, high concealment ability and large coverage area
The effectiveness of our AI-probability hypothesis density (PHD) filter for multi-target tracking in image plane of optical sensor is verified through simulation
Average optimal subpattern assignment (OSPA) for AI-PHD filter of known signal noise ratio (SNR) and unknown SNR case and generic PHD filter are given in Table 2 where the results are divided by ‘/’
Summary
Optical sensors have been widely applied both in important military and civil areas due to their properties of long detection range, high concealment ability and large coverage area. Sensors 2012, 12 scenario, multi-target tracking in the image plane of optical sensors is a very difficult problem. Due to the variation of targets number with time in the field of view of the sensors and the existence of miss detection and dense clutters, multi-target tracking in image plane remains a challenging problem. One of the pioneering techniques was the approach proposed by Colegrove, Lerro and Bar-Shalom [1,2] where the probability data association (PDA) filter utilizing target amplitude was applied in the context of single-target tracking. The significance of target amplitude has been explored for data association of closely spaced targets in [5]. Significant progress has been made recently, the approaches mentioned above are based on data association technique, which requires expensive computational cost in most circumstances
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