Suboptimal Kalman Filters for Target Tracking with Navigation Uncertainty in One Dimension

Abstract

The vast majority of literature on target tracking assumes that the position and orientation of the tracking sensor is stationary and/or known. However, for many applications the sensor is mounted on a moving vehicle, whose motion only can be estimated with a non-negligible uncertainty. In this paper, we suggest seven possible architectures for Kalman filtering in the simplest such scenario that we can construct: Assuming that both the ownship and a single target moves along a straight line according to linear kinematics. Some of the tracking filters are parameterized in the stationary world frame, while others are parameterized the body frame of the ownship. Also, some of the tracking filters take correlations between target state and ownship state into account, while others neglect such correlations. Simulations demonstrate that the suboptimal architectures may or may not reach similar performance as the optimal filter, depending on the process noise of the target and the performance measure chosen. Simulations of multi-target scenarios demonstrate that compensation of navigation uncertainty generally can reduce track-loss rates and OSPA distance.