Research On The Target Tracking And Data Association Techniques With The Information Fusion System

The techniques of target tracking and data association are important topics of the research on information fusion system. Because they have been found wide applications in both military and civil areas, many countries and researchers have paid much attention to the development of the target tracking and data association technology. According to the technologies of target tracking and data association in information fusion, this dissertation mainly involves four aspects: the real-time target tracking of single sensor, multi-sensor multi-target tracking, maneuvering target tracking with multiple passive sensors, and track-before-detect of infrared small target. A series of new methods for engineering applications have been proposed. The main contents of the dissertation are as follows:In Chapter 1, the research background and significance of this dissertation, information fusion system and multi-sensor multi-target system are briefly described. The current research of information fusion,target tracking and track before detect are summarized. Finally, the main achievement and arrangement of this dissertation are concluded.In Chapter 2, the basic theory and mathematics derivation of target tracking problem are described. A summary of the techniques of the multi-target tracking methods appeared in both domestic and foreign publications is provided, and the techniques have been categorized into more than 62 different algorithmic types. At the same time, a comparison of the main performance of the algorithms is analyzed.In Chapter 3, for real-time target tracking in clutter environment, a category novel fast data association method is proposed; including the Maximum Entropy Fuzzy Probabilistic Data Association Filter (MEF-PDAF) and the Maximum Entropy Fuzzy Joint Probabilistic Data Association Filter (MEF-JPDAF). In order to improve the real-time of target tracking, a new weight assignment that the joint association probability is replaced by utilizing the fuzzy membership degree of the target and the measurement is proposed. According to the characteristic maximum entropy fuzzy clustering, the maximum validate distance is defined, which enables the algorithm eliminate those invalidate measurements and reduce the computational load.In Chapter 4, for maneuvering target tracking with multiple passive sensors in clutter environment, a novel interactive multiple model maximum entropy fuzzy probabilistic data association filter is proposed. The structure flow chart of the algorithm is given, and a nonlinear measurement model of multiple passive sensors is...