Hao Deng
Target tracking, unknown bias estimation, OTSKF, RTSKF, robustness
Target tracking with an unknown bias is important in the fields of navigation, trajectory determination, and so on. Kalman filter is simple in principle and widely used, however, it has poor ability for state estimation with unknown bias. To improve its performances, robust two-stage Kalman filter (RTSKF) and optimal two-stage Kalman filter (OTSKF) are adopted for target tracking and unknown bias estimation and their behaviours are compared comprehensively in this work. First, the feasibility of these filters is validated under a low-noise environment. Then, the robustness to resist the noises is investigated when the intensities of process noise and measurement noise are changed from 0.01 to 0.50. Results demonstrate the OTSKF has stronger robustness than RTSKF. Finally, the types of unknown bias are changed to test the flexibility and accuracy to track manoeuvring target.
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