Modern aircraft are getting more and better sensors. As a result of this, the pilots are getting moreinformation than they can handle. To solve this problem one can automate the information processingand instead provide the pilots with conclusions drawn from the sensor information. An aircraft’smovement can be used to determine which class (e.g. commercial aircraft, large military aircraftor fighter) it belongs to. This thesis focuses on comparing three classification schemes; a Bayesianclassification scheme with uniform priors, Transferable Belief Model and a Bayesian classificationscheme with entropic priors.The target is modeled by a jump Markov linear system that switches between different modes (flystraight, turn left, etc.) over time. A marginalized particle filter that spreads its particles over thepossible mode sequences is used for state estimation. Simulations show that the results from Bayesianclassification scheme with uniform priors and the Bayesian classification scheme with entropic priorsare almost identical. The results also show that the Transferable Belief Model is less decisive thanthe Bayesian classification schemes. This effect is argued to come from the least committed principlewithin the Transferable Belief Model. A fixed-lag smoothing algorithm is introduced to the filter andit is shown that the classification results are improved. The advantage of having a filter that remembersthe full mode sequence (such as the marginalized particle filter) and not just determines the currentmode (such as an interacting multiple model filter) is also discussed.

This thesis presents a comprehensive example framework on how current multiple model state estimation algorithms for jump Markov linear systems can be improved. The possible improvements are categorized as: -Design of multiple model state estimation algorithms using new criteria. -Improvements obtained using existing multiple model state estimation algorithms. In the first category, risk-sensitive estimation is proposed for jump Markov linear systems. Two types of cost functions namely, the instantaneous and cumulative cost functions related with risk-sensitive estimation are examined and for each one, the corresponding multiple model estate estimation algorithm is derived. For the cumulative cost function, the derivation involves the reference probability method where one defines and uses a new probability measure under which the involved processes has independence properties. The performance of the proposed risk-sensitive filters are illustrated and compared with conventional algorithms using simulations. The thesis addresses the second category of improvements by proposing -Two new online transition probability estimation schemes for jump Markov linear systems. -A mixed multiple model state estimation scheme which combines desirable properties of two different multiple model state estimation methods. The two online transition probability estimators proposed use the recursive Kullback-Leibler (RKL) procedure and the maximum likelihood (ML) criteria to derive the corresponding identification schemes. When used in state estimation, these methods result in an average error decrease in the root mean square (RMS) state estimation errors, which is proved using simulation studies. The mixed multiple model estimation procedure which utilizes the analysis of the single Gaussian approximation of Gaussian mixtures in Bayesian filtering, combines IMM (Interacting Multiple Model) filter and GPB2 (2nd Order Generalized Pseudo Bayesian) filter efficiently. The resulting algorithm reaches the performance of GPB2 with less Kalman filters.