A New Coarse Gating Strategy Driven Multidimensional Assignment for Two-Stage MHT of Bearings-Only Multisensor-Multitarget Tracking

Abstract

The problem of two-dimensional bearings-only multisensor-multitarget tracking is addressed in this work. For this type of target tracking problem, the multidimensional assignment (MDA) is crucial for identifying measurements originating from the same targets. However, the computation of the assignment cost of all possible associations is extremely high. To reduce the computational complexity of MDA, a new coarse gating strategy is proposed. This is realized by comparing the Mahalanobis distance between the current estimate and initial estimate in an iterative process for the maximum likelihood estimation of the target position with a certain threshold to eliminate potential infeasible associations. When the Mahalanobis distance is less than the threshold, the iteration will exit in advance so as to avoid the expensive computational costs caused by invalid iteration. Furthermore, the proposed strategy is combined with the two-stage multiple hypothesis tracking framework for bearings-only multisensor-multitarget tracking. Numerical experimental results verify its effectiveness.

Optimal Geometry and Motion Coordination for Multisensor Target Tracking with Bearings-Only Measurements

“…Much previous work has been dedicated to developing different estimators for target tracking based on bearings-only measurements in two- and three-dimensional space [ 3 , 4 , 5 , 6 ]. The extended Kalman filter (EKF) is a classical method for the nonlinear tracking problem [ 7 ] but often diverges when the model nonlinearity is strong.…”