Joint spatial registration and multi-target tracking using an extended PM-CPHD filter |
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Authors: | LIAN Feng HAN ChongZhao LIU WeiFeng LIU Jing & YUAN XiangHui SKLMSE Lab MOE KLINNS Lab |
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Affiliation: | , School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; 2School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China |
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Abstract: | An extended product multi-sensor cardinalized probability hypothesis density (PM-CPHD) filter for spatial registration and multi-target tracking (MTT) is proposed. The number and states of targets and the biases of sensors are jointly estimated by this method without the data association. Monte Carlo (MC) simulation results show that the proposed method (i) outperforms, although computationally more expensive than, the extended multi-sensor PHD filter which has been proposed for joint spatial registration and MTT; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense. |
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Keywords: | multi-sensor spatial registration multi-target tracking (MTT) cardinalized probability hypothesis density (PHD) filter random finite set (RFS) |
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