Research On The Multi-target Tracking Techniques Based On Random Set Theory

At present, most research on the multi-sensor multi-target tracking is mainly focusing on the traditional data association algorithm promotion. There are some difficulties such as harsh constraints,"combinatorial explosion"and the NP-Hard problems. With the random sets theory widely applied in information fusion, it provides systematic and rigorous mathematical foundation for multi-target tracking method, and is becoming a powerful tool to solve the multi-sensor multi-target tracking problem. The probability hypothesis density filter (PHDF) based on the random sets theory is one kind of non data association tracking algorithm developed in recent years. This method bypasses the data association question, and overcomes a series of questions caused by the traditional data association algorithm, and is becoming a brand-new algorithm in the multi-target tracking field. As this kind of algorithm has better Bayes significance and approximate result, it can solve number change multi-target tracking problem in the complex environment. Therefore, the article focuses on PHDF algorithm study based on the random sets theory. The prime tasks are as follows:(1) On the basis of the Bayes filter, several typical multi-target tracking filter algorithms are introduced, such as Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF) as well as particle filter (PF). The multi-target state model and observation model in the background of the random sets theory are constructed, by which the state transition probability density function and observation likelihood function are deduced. The intrinsic relationships between Bayes filter and PHDF are discussed based on mathematical principles, so that the PHDF algorithm and corresponding evaluation indicators are analyzed. This section mainly makes a good foundation for the following chapter's algorithm research.(2) Considering the particle probability hypothesis density filter (P-PHDF) algorithm estimate precision lower, filter divergence and other issues, this chapter introduces the UKF algorithm, and proposes unscented particle probability hypothesis density (UP-PHDF) algorithm. This algorithm takes use of the observation information effectively to obtain more superior important density function, further improves the sampling accuracy, and fundamentally solves filter accuracy lower, filter divergence, particle degeneration and so on some questions caused by transition probability density sampling in the P-PHDF algorithm. This algorithm maintains a good filter performance, has stronger adaptability, higher tracking precision, and better robustness and timeliness.(3) To solve multiple maneuvering targets tracking problem under the complex environment, unscented Kalman Gaussian mixture probability hypothesis density filter (UK-GMPHDF) algorithm based on interacting multiple models (IMM) is proposed. The algorithm combines the stronger adaptability of IMM with the higher estimation accuracy and less computation load of UK-GMPHDF to different target maneuvering model, realizes accurate tracking for multiple maneuvering targets under the clutter environment, and greatly improves the accuracy of multiple maneuvering targets tracking.(4) In order to ensure reliability and robustness of the multi-target tracking system, the PHDF algorithm is ranging from a single sensor to multi-sensor case. Considering that the centralized multi-sensor sequential fusion algorithm has minimal information loss, and can achieve the best fusion effect, in this paper a algorithm combined the centralized sequential multi-sensor with the UP-PHDF algorithm is proposed. It is not only suitable for the random non-linear non-Gauss system, but also manifests heterogeneous sensor superiority.(5) To improve the application field of the multi-sensor fusion, the fuzzy C-means (FCM) clustering fusion algorithm based on the UK-GMPHDF is proposed for the distributed multi-sensor multi-target tracking. In the algorithm, the UK-GMPHDF is used to complete local state estimation of local sensors, then the FCM algorithm is used to fuse the local state estimation and result global state estimation. This algorithm is able to enhance target number changing in the clutter situation, and has stronger robustness and higher tracking performance.