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This paper considers the sensor selection problem for target tracking in large-scale sensor networks. We propose a new sensor selection strategy based on dual-criterion optimization. Both the bias change detection and information gain maximization are considered as criteria in our proposed sensor selection strategy. This new approach extends the sensor selection problem from single criterion optimization...
We develop an Expectation-Maximization (EM) algorithm for the simultaneous tracking and shape estimation of a star-convex object based on multiple spatially distributed measurements. In order to formulate the problem within the EM framework, the unknown measurement sources on the object are modeled as hidden variables. As the measurement sources are continuous quantities, we develop a suitable discretization...
This paper is concerned with the fusion estimation problem for multi-sensor discrete time-invariant linear systems with multiple time delays and colored measurement noise. A fast sequential covariance intersection (SCI) fusion Kalman filter is given based on the augmented Kalman filter in the linear minimum variance sense, which avoids the calculation of the cross covariance matrices between local...
With the ubiquity of information distributed in networks, performing recursive Bayesian estimation using distributed calculations is becoming more and more important. There are a wide variety of algorithms catering to different applications and requiring different degrees of knowledge about the other nodes involved. One recently developed algorithm is the distributed Kalman filter (DKF), which assumes...
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