Data association via logistic regression model for multiple target tracking problems

Abstract

In the multiple target tracking scenarios, the correct matching between targets and measurements is critical. There have been many approaches to resolve this problem called data association. In this paper, a regression method is proposed to resolve the data association problem. In the logistic regression model, nine potential predictor variables are designed which are related to the geometric information of measurements and estimated states of multiple targets, including the distance, intersection angle of position vectors and smoothness of tracks at current time instant and several previous time steps, and the dependent variable is the association probability of matching the measurement with all targets. The regression coefficients are trained through a designed multiple target tracking system. For the new unknown tracking systems with the given number of tracked targets, the measurement having the highest association probability with a target is considered as the true measurement about such target using the trained empirical regression model. Moreover, various filtering algorithms can be invoked to tracking targets. Simulation studies show the proposed novel mechanism for tackling with data association problem in multiple target tracking is effective.