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A novel method involved the time-varying tracking model under the nonlinear state-space evolved system is presented, in which the expectation-maximization (EM) algorithm is used to identify the state transition matrix f and the process noise covariance Q online. The typical maneuvering models, as described, essentially, are prior models and use fixed and constant evolved matrix and designed noise...
Probabilistic multi-hypothesis tracking (PMHT) is an algorithm for tracking multiple targets when measurement-to- target assignments are unknown and must be jointly estimated with the target tracks. Multi-frame assignment PMHT (MF- PMHT) is an algorithm designed to mitigate some performance problems associated with PMHT. In MF-PMHT, the PMHT algorithm is applied to multi-frame sequences in the last...
Bayesian networks (BNs) represent joint space probabilities compactly and enable one to carry out efficient inferencing. Although the Dempster-Shafer (DS) belief theoretic framework captures a wider class of imperfections, its utility in such graphical models is limited. This is mainly due to the requirement of having to maintain a basic probability assignment (BPA) for the whole power set of propositions...
In this paper, first an enhanced neuro-fuzzy method for modeling nonlinear system is presented In this method we use EM algorithm for identification of local models, which gain us model mismatch covariance. The achieved model can be stated in state space model as a linear time-varying system. As the noise and model mismatch covariance is known, Kalman filter can be easily used for centralized estimation...
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