Abstract: This paper presents a new tracking filter capable of soft switching between two kinematic target models without requiring any a prior knowledge of the target state's transition probability matrix. The target models used are both constant velocity models, one with a low state process noise and one with a high state process noise. Simulations are performed to show the soft switching capability of the new filter as well as its performance. The newly derived filter significantly outperforms a well-known variable dimension filter. The result of this paper constitute a first step toward designing a new class of filters that are capable of soft switching between different target kinematic models without requiring a priori knowledge of the target state's transition likelihoods. !8
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