Improved Nonlinear Filtering For Target Tracking
Particle Filtering: Basics, Concepts and Improvements
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Particle filtering is a new nonlinear stateestimation technique that aims to directlyapproximate the posterior distribution of thesystem. This technique was introduced to theengineering community in the early years of 2000.Since then it has drawn significant attentions due toits accuracy, robustness and flexibility in variousnonlinear/non-Gaussian estimation applications, suchas target tracking, robot localization and mapping,communications, sensor networks, computer vision andothers. Latest research has shown that particlefilter based algorithms can greatly improve theestimations over convent...
Particle filtering is a new nonlinear stateestimation technique that aims to directlyapproximate the posterior distribution of thesystem. This technique was introduced to theengineering community in the early years of 2000.Since then it has drawn significant attentions due toits accuracy, robustness and flexibility in variousnonlinear/non-Gaussian estimation applications, suchas target tracking, robot localization and mapping,communications, sensor networks, computer vision andothers. Latest research has shown that particlefilter based algorithms can greatly improve theestimations over conventional methods, suchas extended Kalman filter (EKF). This bookintroduces the basic concept of particle filtering,its advantages and limitations as well as variousmethods to improve particle filters. The analysisprovided by this book should shed some light on howto design advanced particle filter trackingalgorithms.
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