Bearings only single-sensor target tracking using Gaussian mixtures

This paper presents a new approach for single sensor tracking using passive bearings only measurements. Gaussian mixture measurement presentation, together with a track splitting algorithm, allow space-time integration of the target position uncertainty with a simple algorithm. The bearings-only measurements are incorporated into track as they arrive using a dynamic bank of linear Kalman filters. While this approach is applicable to the case with the target detection, data association and multitarget issues, this paper concentrates on the target trajectory estimation using associated measurements. A simulation study demonstrates the benefits of this approach.