Abstract: Several multiple model techniques have been applied to the tracking of maneuvering targets. The two techniques which provide the best tracking performance for maneuvering targets are the Second Order General Pseudo-Bayesian (GPB2) and Interacting Multiple Model (IMM) algorithms. In both algorithms, the dynamics of the system is represented by multiple models which are hypothesized to be correct and model switching probabilities governed by a first order Markov process. The authors have developed an extension of the IMM algorithm, the second order Interacting Multiple Model (IMM@) algorithm, which provides improved tracking performance when compared to that of the IMM and GPB2 algorithms for applications with large measurement errors and low data rates. In the IMM2 algorithm, the state estimate is computed under each possible model hypothesis for the two most recent sample periods with each hypothesis using a different combination of the previous model-conditional estimates. Thus, the IMM2 algorithm requires r$+2$/ filters for r models. The development of the IMM2 algorithm is given along with a summary of multiple model estimation for tracking maneuvering targets and simulation results for the IMM, GPB2, and IMM2 algorithms.!15
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