Abstract
This paper is devoted to the problem of state estimate of discrete-time stochastic systems. A low-complexity and high accuracy algorithm is presented to reduce the computational load of the traditional interacting multiple model algorithm with heterogeneous observations for location tracking. By decoupling the x and y dimensions to simplify the implementation of location, updated information is iteratively passed based on an adaptive fusion decision. Simulations show that the algorithm is more computationally attractive than existing multiple model methods.
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