HYBRID EXTENDED KALMAM FILTER FOR TARGET TRACKING UNDER PARTIAL MEASUREMENT CONDITIONS

Abstract

This paper presents a flexible Hybrid Extended Kalman Filter (HEKF) framework for solving the target tracking problem under realistic conditions, where measurements are obtained in polar coordinates. The proposed framework consists of a primary HEKF and an independent state predictor module. The HEKF works within two measurement modes: Full Measurement (FM) and Angle-Only (AO). An adaptive strategy is integrated into the framework, incorporating a synchronized mode-switching mechanism, a state prediction and fusion process, and a degradation-handling mechanism to maintain tracking reliability when measurement quality deteriorates. The estimation confidence of target coordinates is evaluated through the state covariance, providing effective support for operator decision-making. Simulation results based on a near-realistic scenario demonstrate that the proposed HEKF framework can maintain high-accuracy target position estimation during tracking phases and exhibits robustness and fast recovery when the system returns to the FM mode.