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This paper considers the state estimation of Markovian jump linear systems with random parameters and estimate feedback. The state estimate at the previous epoch is introduced into the dynamical model to depict some phenomena that the system evolvement may depend on the most recent estimate. Then, the linear minimum mean square error estimator is derived for the considered system. A filtering framework...
This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The...
We investigate the problem of determining the trajectory that an observer should follow to be able to accurately track a target in a bearings-only measurements context. We assume that the target's motion is uniform and that the measurements are corrupted by an additive Gaussian white noise. Though, in theory, this process is observable if the observer maneuvers with turns or accelerations, the quality...
Moving route prediction offers important benefits for many emerging location-aware applications such as target advertising and urban traffic management. A common approach to route prediction is to match similar trace recordings from a larger volume of historical trajectories, and return the targeted recorded path as desired answer. However, due to privacy concerns, incentive mechanism and other reasons,...
Under the common state space model for tracking a maneuvering target, the tracker needs to adapt its state transition model timely to match the target maneuver, which is usually carried out by finding the best one from a bank of candidate Markov models or employing all of them simultaneously but assigning different probabilities. Both methods suffer from time delay for confirming the target maneuver...
This paper proposes a new approach for constrained multiple model (MM) maximum a posteriori (MAP) estimation through the expectation-maximization (EM) method by using our previously developed constrained sequential list Viterbi algorithm (CSLVA). The approach is general and applicable for any type of constraints provided they are verifiable. Specific algorithms for implementation are designed, and...
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