Sensor Control for Selective Object Tracking Using Labeled Multi-Bernoulli Filter
Sabita Panicker, Amirali Khodadadian Gostar, Alireza Bab-Haidashar, Reza Hoseinnezhad · 2018
With the recent advent of labeled random finite set filters, it is now possible to not only estimate the number of objects and their states, but also track their trajectories all within the stochastic filtering scheme. This paper investigates how the objects label information returned by a labeled multi-Bernoulli filter can be effectively used for sensor control purposes. The main focus is on selective multi-object tracking applications where objects with particular labels are of high priority, and the sensor needs to be controlled to achieve maximum confidence in tracking performance of the filter for those objects. We formulated and examined two novel solutions. The first is based on maximization of the confidence in what the filter returns in relation to the existence of the objects of interest. The second solution is based on maximization of the confidence in what the filter returns in relation to the both existence and the states of the objects of interest. We also present an intuitive solution for tracking scenarios when some of the targets of interest temporarily disappear then reappear. Simulation results indicate how the proposed methods can lead to significant improvements in terms of tracking accuracy of objects of interest, compared to using the generic (non-selective) sensor control methods.