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The particle filter is a Bayesian estimation technique based on Monte Carlo simulation. It is ideal for non-linear, non-Gaussian dynamical systems with applications in many areas, such as computer vision, robotics, and econometrics. Practical use has so far been limited, because of steep computational requirements. In this study, we investigate how to design a particle filter framework for complex...
We present the design, analysis, and real-time implementation of a distributed computation particle filter on a graphic processing unit (GPU) architecture that is especially suited for fast real-time control applications. The proposed filter architecture is composed of a number of local subfilters that can share limited information among each other via an arbitrarily chosen abstract connected communication...
Distributed linear estimation theory has received increased attention in recent years due to several promising industrial applications. Distributed nonlinear estimation, however is still a relatively unexplored field despite the need in numerous practical situations for techniques that can handle nonlinearities. This paper presents a unified way of describing distributed implementations of three commonly...
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