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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...
In this paper a novel multiple model particle filter algorithm for tracking ground targets on constrained paths is developed The algorithm is designed to let the different modes be represented by constrained likelihood models, whereas the state dynamics are the same for all models. The mixing procedure is performed over the likelihood models and the mixing parameters are calculated in a standard interacting...
Target tracking from incomplete measurements of distinct sensors in a sensor network is a task of data fusion, present in a lot of applications. Difficulties in tracking using extended Kalman filters lead to unstable behavior, mainly caused by difficult initialization. Instead of using numerical batch-estimators, we offer an analytical approach to initialize the filter from a minimum number of observations...
The hybrid SIR joint particle filter has been developed as an effective approximation of the exact Bayesian filter for maintaining tracks of multiple maneuvering targets from unassociated measurements. This paper further develops this approach for the situation of limited sensor resolution and two maneuvering targets. For this problem the exact Bayesian filter recursion is characterized, and is subsequently...
The interacting multiple model filter has long been the preferred method to handle multiple models in target tracking. The filter finds a suboptimal solution to a problem, which implicitly assumes that immediate model shifts have the highest probability. We argue that this model-shift property does not capture the typical nature of maneuvering targets, namely that changes in target dynamics persist...
Given an area where an unknown number of unaccounted radioactive sources potentially exist, and using gamma- radiation count measurements collected at known locations within this area, the problem is to estimate the number of sources as well as their locations and intensities. Two approaches are investigated. The first is based on the maximum likelihood estimation and the generalised maximum likelihood...
In multi-sensor multi-target bearings-only tracking we often see false intersections of bearings known as ghosts. When the bearing measurements from each sensor have been associated to form sequences termed threads, the problem is to associate pairs of threads to identify the true target intersections. In this paper we present two algorithms: (i) classical bayesian thread association (CBTA) and (ii)...
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