Joint Multi-Sensor Calibration and Multi-Target Tracking Based on Hierarchical Point Processes

Abstract

On the basis of the multi-group multi-target probability hypothesis density (MGMT-PHD)filter and hierarchical point processes modeling, a new joint multi-sensor bias and multi-target state estimation algorithm is proposed in this paper. The parent processes is the set of the multi-sensor biases, the daughter processes is the set of multi-target state spaces corresponding to the multiple sensors. By separating the two interacting point processes, a large computational complexity arising from jointly estimating multi-sensor biases and multitarget states in an augmented high dimension state space for the existing methods can be avoided. When the MGMT-PHD filter is used to jointly estimating multi-sensor biases and multi-target states, the number of the sensors is known, that is to say it is not required to estimate the number of elements in the parent processes. In addition, each sensor independently obtains the corresponding measurement set, which means that the partition of the measurement set is determinate and unique. The implementation of the MGMT-PHD filter can be greatly simplified. Then, the particle implementation of the MGMT-PHD filter is proposed to jointly estimating the multi-sensor biases and multi-target states under nonlinear conditions.