Abstract
As single sensors cannot detect and track targets with low detection probability,a new multisensor box particle probability hypothesis density filter is proposed in this paper.The MS-BOX-PHD filter converts and fuses multiple sensor measurement sets into a new set,and the multitarget states are predicted and updated using a box particle probability hypothesis density filter.Numerical experiments show that the MS-BOX-PHD filter can estimate the state and number of multitargets when the target detection probability is low,unlike a single sensor box particle probability hypothesis density filter.Compared with the multisensor standard probability hypothesis density filter with interval measurement,the computational efficiency increased by 38.57%for the same tracking performance.
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