Research On Methods For Passive Multi-sensor Target Tracking

Because of the merit of concealment and the capability to avoid attack for passive detection in comparison with conventional radar detection, passive detecting and tracking by utilizing the signal emitted from the target have been one of the hot topics of the modern defense system. Constructing the system of passive multi-sensor so as to achieve the passive target detecting and tracking is significant to enhance the capability of defense system. The passive multi-sensor target tracking, as a key technology of passive multi-sensor defense system, is studied and some effective new methods are presented in the thesis. The thesis is classified into seven chapters and their contents are outlined as follows.In Chapter 1, the background and importance of the research on the passive multi-sensor target tracking are explained; the structure of passive multi-sensor system is introduced. The status quo of the research on target locating and tracking techniques is reviewed. Finally, the main achievements and arrangements of the thesis are concluded.In Chapter 2, the basic theory of target tracking is described, including the common dynamical models and filters.In Chapter 3, target tracking with multiple passive sensors is discussed in deep. Begin with the target moving in constant velocity, dynamical model of the target and the measurement model of multiple passive sensors are set up. For the nonlinear relationship between the state of the target and the bearings measurements, the algorithms of passive multi-sensor target tracking based on extended kalman filter and unscented kalman filter have been deduced, respectively. And the algorithm of passive multi-sensor target tracking based on the least squares fusion is proposed. Non maneuvering target tracking with high precision is achieved in the chapter.In Chapter 4, the algorithm of maneuver detection based on higher-order cumulants is proposed. As the higher-order cumulants are blind to Gaussian noise (white or colored), the behavior of the maneuver becomes obvious and easy to be detected in the higher-order cumulant domains. Taking the sliding window processing, the maneuver detection algorithm is real-time without any time delay. On the basis of the maneuver detection algorithm, the maneuver target tracking for multiple passive sensors is studied further. In Chapter 5, for maneuvering target tracking with multiple passive sensors, a least squares adaptive algorithm based on the current statistical model is presented, in which the state of the target is approximately estimated by least squares algorithm at first, and then a current statistical model and an adaptive algorithm are employed. An extended kalman filter adaptive algorithm is presented in combination with the extended kalman filter and the current statistical model. A root square unscented kalman filter adaptive algorithm is presented in combination with the root square unscented kalman filter and the current statistical model. As a time variant model, the current statistical model is able to describe the target maneuver more reasonable, the above three algorithms has ability to track the maneuver target more effectively, especially does the least squares adaptive algorithm.In Chapter 6, the unscented kalman filter in combination with the interacting multiple model applied to the passive multi-sensor maneuver target tracking, a novel unscented kalman filter interacting multiple model algorithm is proposed. It can track maneuver target in multi-sensor bearings-only tracking. In addition, in application to the maneuver target tracking with the least squares fusion, the least squares interacting multiple model algorithm is presented. Finally, a comparison of the algorithmic performance is presented.In Chapter 7, a summarization to the whole thesis is given and the prospect of the issues concerned in the field is also made.