The purpose of this chapter is to present state estimation techniques than can “adapt” themselves to certain types of uncertainties beyond those treated in earlier chapters—adaptive estimation algorithms. One type of uncertainty to be considered is the case of unknown inputs into the system, which typifies maneuvering targets. The other type will be a combination of system parameter uncertainties with unknown inputs where the system parameters (are assumed to) take values in a discrete set. The input estimation with state estimate correction technique is presented. The technique of estimating the input and, when “statistically significant,” augmenting the state with it (which leads to variable state dimension), is detailed. These two algorithms and the noise level switching technique are later compared. The design of an IMM estimator for air traffic control (ATC) is discussed in detail. Guidelines are also developed for when an adaptive estimator is really needed, i.e., when a (single model based) Kalman filter is not adequate. The chapter concludes with a brief presentation of the use of the extended Kalman filter for state and system parameter estimation. A problem solving section appears at the end of the chapter.
Adaptive Estimation and Maneuvering Targets
2002-01-04
70 pages
Article/Chapter (Book)
Electronic Resource
English
term project , input estimation , design of an IMM estimator , maneuvering targets , multiple model approach , ATC tracking , variable state dimension approach , EKF for simultaneous state and parameter estimation , adjustable level process noise , adaptive estimation , comparison of adaptive estimation methods , problems
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