PROJECT TITLE :

IMM Forward Filtering and Backward Smoothing for Maneuvering Target Tracking

ABSTRACT:

The interacting multiple model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, that uses measurements beyond the present estimation time, provides better estimates of target states. Various strategies are proposed for multiple model (MM) smoothing in the literature. A new smoothing method is presented here which involves forward filtering followed by backward smoothing whereas maintaining the elemental spirit of the IMM. The forward filtering is performed using the quality IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode-conditioned smoother uses normal Kalman smoothing recursion. The ensuing algorithm provides improved however delayed estimates of target states. Simulation studies are performed to demonstrate the improved performance with a maneuvering target scenario. Results of the new methodology are compared with existing strategies, particularly, the augmented state IMM filter and therefore the generalized pseudo-Bayesian estimator of order two smoothing. Specifically, the proposed IMM smoother operates simply like the IMM estimator, which approximates $N^a pair of$ state transitions using $N$ filters, where $N$ is the quantity of motion models. In contrast, previous approaches require $N^a pair of$ smoothers or an augmented state.


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