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The problem of tracking objects moving in Cartesian space with sensors delivering polar measurements has been under investigation of several researchers for quite some time now. Different proposals for using measurement conversion techniques in combination with a linear Kalman filter have been made in order to reduce the range bias that shows up in the filter estimates when a Cartesian pseudo-measurement...
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 focus of this paper is on examining the accuracy of two existing state vector fusion methods, weighted covariance fusion (WCF) and information matrix fusion (IMF), in a multi-sensor environment for computing the fused estimates from distributed Kahnan filters tracking a single maneuvering target. Each sensor tracker utilized in the Reference Cartesian Coordinate System (RCCS) is described for...
Recently several new results for Cramer-Rao lower bounds (CRLB's) in dynamical systems have been obtained. Several different approaches and approximations have been presented. For the general case of target tracking with a detection probability smaller than one and possibly in the presence of false measurements, two main approaches have been presented. One is the so called information reduction factor...
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