Probability Threshold Scheme in Fast Model-set Adaptive IMM
نویسندگان
چکیده
This study is devoted to overcome the underflow problem and poorly cost-effective limitation of model-set adaptive IMM algorithm. Cause of underflow problem in NovelIMM is addressed firstly, based on which an underflow prevented selection probabilities (UPSP) algorithm is presented to solve this problem. This paper then presents a fast model-set adaptive (FAIMM) IMM algorithm based on steady state Kalman filters that decrease the computational burden greatly while keeping acceptable tracking accuracy. Finally, the threshold choosing strategy of UPSP algorithm is presented, which could make the FAIMM algorithm achieves ideal performance. Simulation results demonstrate that the FAIMM algorithm can be an effective estimator in real-time application.
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