This paper proposes several sensor control strategies via Gaussian mixture multi-target filter (GM-MTF) with random finite set.First

on the basis of the cubature Kalman Gaussian mixture multi-target nonlinear filter

the global information gain of the GM-MTF is deduced through the Bhattacharyya distance between the two Gaussian distributions.Then

taking advantage of this information distance

this paper proposes a corresponding sensor control strategy.Furthermore

a joint sampling method of Gaussian particle is designed to sample the predicted Gaussian component of multi-target filter.Subsequently

a set of weighted particles are used to approximate the multi-target statistical characteristic

and their weights are updated with the ideal measurement set.Next

a Rnyi divergence based sensor control strategy which has better adaptability is proposed.Finally

a detailed Gaussian mixture implementation of the posterior expected number of targets (PENT) is given.Simulation results verify the effectiveness of these algorithms.