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Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm
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DOI：10.1109/TSMC.2019.2922305.png)
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Abstract
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Conventionally, the transition probabilities in the interacting multiple model (IMM) are often fixed based on the prior information. However, this conservative setting may result in inaccurate state estimations. To solve this problem, a Bayesian-based online correction function is proposed in this paper, which can adaptively adjust the transition probabilities. To deal with the response lag and the short-term peak estimation error problem during the respond to model jump, a model jumping threshold is defined, so that the current information of the models can be fully utilized by the IMM algorithm and the correction function of the transition probabilities can be further improved. Subsequently, an adaptive transition probability-based parallel IMM algorithm is proposed in this paper. Finally, three maneuvering target tracking simulations are conducted to verify the performance of the proposed algorithm, the results show that the proposed algorithm can improve the response speed of the system model jump and the state estimation accuracy. The effectiveness and feasibility of the algorithm are proven.
Keywords:
Adaptation models
State estimation
Mathematical model
Predictive models
Markov processes
Covariance matrices
Interacting multiple model (IMM)
model jumping threshold
posterior information
transition probability matrix (TPM)
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