Study On Low Dynamic Target Localization And Tracking Algorithms In Near Space Using Ground Based Radar

Near space will open up a new battlefield for the future war, especially the development and application of near space vehicle may make profound influence on the future of the whole operation system and operational thought, therefore, strengthening the study on near space vehicle location and tracking has important theoretical significance and application value. Passive location technology based on its good concealment, far operating distance, strong survival ability and anti-stealth ability etc, plays an increasing role in the military confrontation, therefore its application involves in navigation, aviation, aerospace and electronic warfare. In this paper, the passive location is used in near space low dynamic target localization and tracking, the main work of this paper are illustrated as follows:(1) The research background and significance of near space low dynamic target localization and tracking using ground based radar, summarizes the research status of the near space vehicles at home and abroad, introduces the basic knowledge of multi-station passive location.(2) The algorithm principles and parameter design of4-station TDOA location system are deep reserached, simulate and verify various factors affecting the positioning accuracy, such as the baseline length, target height, position error, time measurement error and altitude difference between Radar platforms. Through comparing the location accuracy of four different stations, eventually selected Star-modal Station to track the moving target.(3) As the single model has poor performance in tracking near space low dynamic targets when they are being maneuvered, TDOA-CVCS algorithm is proposed, it combines with4-station TDOA location system star-model station, uses Constant Velocity (CV) model and "Current" Statistic (CS) model to construct the model set, estimated the new filter input value by interact the former state estimation, then use the EKF algorithm to filter and predict, get the target state estimate and covariance estimate corresponding to each filter output, finally update probability model and fuse each filter’s output to get the final target state estimate and covariance estimates. Simulation results show that, TDOA-CVCS algorithm can not only maintain faster response speed and high tracking accuracy of the CS model when tracking maneuvering targets, but also can improve the tracking performance of non-maneuvering target, and the computational complexity is low.(4) As the TDOA-CVCS algorithm has poor performance in tracking near space low dynamic targets circular motion, TDOA-CVCSCT algorithm is proposed, the algorithm increases the Coordinated Turn (CT) model to the model set of TDOA-CVCS algorithm, improve the calculation method of angular velocity in circular motion. Simulation results show that, TDOA-CVCSCT algorithm can inhibit the measurement error, improve poor performance of the TDOA-CVCS algorithm in tracking maneuvering targets, it is effective in tracking complex maneuvering targets.