Application of improved particle filter in multiple maneuvering target tracking system

Abstract

Target tracking has been widely used in different fields such as surveillance, automated guidance systems, and robotics in general. The most commonly used framework for tracking is that of Bayesian sequential estimation. This framework is probabilistic in nature, and thus facilitates the modeling of uncertainties due to inaccurate models, sensor errors, environmental noise, etc. However, the application of the Bayesian sequential estimation framework to real world tracking problems is plagued by the difficulties associated with nonlinear and non-Gaussian situation. Realistic models for target dynamics and measurement processes are often nonlinear and non-Gaussian in type, so that no closed-form analytic expression can be obtained for tracking recursions. For general nonlinear and non-Gaussian models, particle filter has become a practical and popular numerical technique to approximate the Bayesian tracking recursions. This is due to its efficiency, simplicity, flexibility, ease of implementation, and modeling success over a wide range of challenging applications. The purpose of this thesis is to develop effective particle filter based methods for target tracking application. The research work consists of four parts: i) particle filter based maneuvering target tracking algorithms, ii) particle filter based multiple target tracking algorithms, iii) particle filter based multiple maneuvering target tracking algorithms, and iv) the experiment of target tracking system based on multi-sensor fusion on a mobile robot platform.