Bearings-only multi-target tracking using an improved labeled multi-Bernoulli filter

Most classical bearing-only target tracking algorithms model the measurement likelihood by one Gaussian distribution. The effectiveness of one Gaussian distribution model relies heavily on the accuracy of the predicted target position. However, due to the high nonlinearity of the bearing-only measurement, the predicted target position is mostly inaccurate before the target state observability is established. As a consequence, some classical nonlinear filters become not applicable for tracking bearing-only targets, especially when the measurements of multiple targets and clutter are present. The published bearings-only multiple-target tracking algorithms suffer from either the estimation inaccuracy or lack of track trajectories. Motivated by the problems mentioned above, we propose an improved labeled multi-Bernoulli filter for the goal of reducing estimation error under the premise that track trajectories are guaranteed. The proposed method divides the bearing measurement uncertainty into several measurement components that the measurement likelihood can be approximated by a Gaussian mixture. By assigning each track a unique label, the previous scan estimations and current scan measurements are associated and the track trajectories become available. Simulation results show that the proposed method considerably reduces estimation error. Further, various scenario parameters are investigated to validate the effectiveness of the proposed method.