| Multiple radar identify and track the ground maneuvering target technology is widely used in military and civilian fields. Domestic and foreign scholars have researched on this issue in-depth and achieved fruitful results. Tracking environment and the target are constantly changing, so single radar detection capability is hard to meet the requirements of the tracking system. In order to achieve the full range of tracking for monitoring target, building networks of multiple radar is needed. Ground maneuvering target movement has a strong mobility and uncertainty which put forward higher requirements on the performance of the tracking system. In this paper, we put the multiple radar identification and tracking ground maneuvering target as the background, study deep in the tracking filter technology, data related technology and data fusion technology.Firstly, multiple radar identification and filtering method for tracking ground maneuvering targets are studied, include basic kalman filtering method, nonlinear least squares filtering active decoy target recognition method, adaptive kalman filtering method as well as the improvement of the method, and the simulation results show that the improved method is effective, the accuracy of the tracking is improved.Secondly, research on the multiple radar tracking the ground maneuvering target data fusion method. Analysis and research the combination of dynamic optimal weight and least squares filter method for synchronization track fusion, and simulation results show it effectiveness. Both the based on the optimal weighted multi-model asynchronous track fusion(MMASTF)method and the based on the state error covariance trace minimum criteria asynchronous track fusion(TMASTF)method are analyzed. Simulation results prove the MMASTF fusion method which has a practical applying on multiple radar track the ground maneuvering target is superior to the TMASTF fusion method.In this paper, the theory and method of multiple radar identification and tracking ground maneuvering targets are expounded,we also research on the adaptive filtering method,the data associated methods and data fusion method. In the end, we put forward the direction of the subject in the future to continue to study. |
