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.