NDLI: Second Order Interacting Multiple Model Algorithm for Systems With Markovian Switching Coefficients

Content Provider	IEEE Xplore Digital Library
Author	Blair, W.D. Kazakos, D.
Copyright Year	1993
Description	Author affiliation: Dept. of Electrical Engineering, University of Virginia, Charlottesville, Virginia 22908 (Kazakos, D.) \|\| Research and Technology Department, Naval Surface Warfare Center, Dahlgren, Virginia 22448-5000 (Blair, W.D.)
Abstract	An important problem is the estimation of the state of a linear system with Markovian switching coefficients. In this problem, the dynamics of the system is represented by multiple models which are hypothesized to be correct. The Interacting Multiple Model (IMM) algorithm is a novel approach to merging the different model hypotheses. In the IMM algorithm, the state estimate is computed under each possible model hypothesis over the most recent sampling period with each model using a different combination of previous model-conditioned estimates. In this paper, the second order Interacting Multiple Model (IMM2) algorithm is developed for estimating the state of a linear system with Markovian switching coefficients. In the IMM2 algorithm, the state estimate is computed under each possible model hypothesis over the two most recent sampling periods with each model hypothesis using a different combination of the previous model-conditioned estimates. Simulation results are given for a target tracking example to demonstrate the performance of the IMM2 algorithm relative to that of the IMM and second order Generalized Pseudo-Bayesian algorithms.
Starting Page	484
Ending Page	488
File Size	440793
Page Count	5
File Format	PDF
ISBN	0780308603
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-06-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	State estimation Filters Linear systems Sampling methods Filtering Merging Computational modeling Target tracking Acceleration
Content Type	Text
Resource Type	Article

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