Improved Nonlinear Filtering For Target Tracking

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.

Autorentext

Dr. Yan Zhai: Ph.D in ECE from the Univ. of Oklahoma in 2007. His research area is signal processing. He is now with Schlumberger, TX. Dr. Mark Yeary: Ph.D.E.E from Texas A&M University in 1999. Currently, he is a tenured Associate Professor in the Univ. of Oklahoma. His research interest is signal processing in weather radar applications.

Klappentext
Particle filtering is a new nonlinear state estimation technique that aims to directly approximate the posterior distribution of the system. This technique was introduced to the engineering community in the early years of 2000. Since then it has drawn significant attentions due to its accuracy, robustness and flexibility in various nonlinear/non-Gaussian estimation applications, such as target tracking, robot localization and mapping, communications, sensor networks, computer vision and others. Latest research has shown that particle filter based algorithms can greatly improve the estimations over conventional methods, such as extended Kalman filter (EKF). This book introduces the basic concept of particle filtering, its advantages and limitations as well as various methods to improve particle filters. The analysis provided by this book should shed some light on how to design advanced particle filter tracking algorithms.