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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...
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...
Particle filters are a widely used tool to perform Bayesian filtering under nonlinear dynamic and measurement models or non-Gaussian distributions. However, the performance of particle filters plummets when dealing with high-dimensional state spaces. In this paper, we propose a method that makes use of multiple particle filtering to circumvent this difficulty. Multiple particle filters partition the...
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