Two novel sensor control schemes for multi-target tracking via delta generalised labelled multi-Bernoulli filtering

摘要

The study addresses the sensor control problem for multi-target tracking via delta generalised labelled multi-Bernoulli (δ-GLMB) filter, and proposes two novel single-sensor control schemes. One is that the Rényi divergence is used as the objective function to measure the information gain between the predicted and posterior densities of the δ-GLMB filter, and it is superior for the overall performance of the system. Since most of the sensor control schemes, including the scheme the authors proposed, are faced the curse of computation, thus the other novel scheme is proposed. This scheme, in which the sum of the statistical distances between the predicted states of targets and sensor is used as the objective function, evades the updated step of the multi-target filter, when computing the objective function for each admissible action. Moreover, these two sensor control schemes are applied to a distributed multi-sensor system, in which the proposed schemes are used for each sensor node and the generalised covariance intersection method is used to compute the fused multi-target posterior density. Finally, they adopt the sequential Monte-Carlo method in bearing and range multi-target tracking scenarios to illustrate the effectiveness of the proposed methods.