Bearings-Only Tracking Using Augmented Ensemble Kalman Filter

Abstract

Tracking targets with bearings-only measurement is a great challenge caused by poor observability and highly nonlinear estimation. In this brief, a novel augmented ensemble Kalman filter (AEnKF) is presented to address this bearings-only tracking problem. Different from the conventional ensemble Kalman filter (EnKF), the AEnKF overcomes the limitation of the linear measurement update rule in the linear minimum mean-square error (LMMSE) framework. The AEnKF utilizes a nonlinear transform of the measurement, called uncorrelated conversion (UC), to augment the measurement space. This conversion serves as a pseudomeasurement and is uncorrelated with the original measurement statistically. Unlike other UC filters based on the Gaussian assumption in the existing literature, the AEnKF does not impose any assumption on the probability density of the measurement by using generalized orthogonal polynomials to construct the UCs in a systematic way. The simulation results show that the AEnKF outperforms the conventional EnKF and other UC filters in the bearings-only tracking problem.