分布式传感器网络下弹道目标跟踪算法研究

项目名称： 分布式传感器网络下弹道目标跟踪算法研究

项目编号： No.61203238

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 付小雁

作者单位： 北京航空航天大学

项目金额： 25万元

中文摘要： 本项目以提高对弹道目标跟踪的实时性和精确性为目标，针对传感器网络覆盖特点及弹道目标运动特性，提出分别将随机服务系统理论和智能识别理论与目标跟踪理论相结合，研究分布式传感器网络条件下弹道目标跟踪的状态估计及航迹融合算法。主要内容包括：（1）建立携带目标不同阶段运动特征及传感器网络覆盖特点的弹道目标跟踪系统模型；（2）研究传感器网络数据处理系统中最佳融合时机和时长问题，提出基于随机服务系统理论的最优航迹关联算法，并分析算法的鲁棒性；（3）研究粒子滤波中各待定参数与估计精度的函数关系，提出基于智能识别理论的粒子滤波算法；（4）对所提算法进行仿真，并在无线传感器网络移动机器人平台上进行物理验证。本项目的研究不仅能丰富目标跟踪理论，而且能为分布式卫星，导弹防御等实际系统提供设计方案，具有重要的科学意义和应用价值

中文关键词： 传感器网络；交互式多模型；在轨跟踪；并行化；

英文摘要： This project is devoted to state estimation of ballistic targets with distributed sensor network. Some algorithms will be proposed using intelligent recognition method for nonlinear filtering and queuing theory for data fusion in accordance with the characteristics of ballistic target motion and network coverage ratio. There are four main themes within the proposed research. The first is to construct the model of ballistic target tracking system involving the motion characteristics of ballistic target and coverage ratio of network. The second theme is to explore the existence and robustness of optimum track-to-track fusion strategies in the target tracking system. In this direction the proposed research involves optimum queuing theory approach and thus requires a detailed study of the corresponding optimization problems: opportune fusion moment and fusion time to fulfill the desired accuracy of target state estimation. The third theme is to explore the function relationship between the parameters and the accuracy of particle filtering. The research in this direction leads towards novel particle filtering, which involves intelligent recognition method for any switching nonlinear system. The last theme is to confirm the feasibility of the induced tracking algorithms based on the above strategies and filtering. In

英文关键词： sensor networks；IMM；tracking on orbit；parallelization；