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