基于有限集统计的雷达组网弱信噪比目标检测跟踪理论研究

项目名称： 基于有限集统计的雷达组网弱信噪比目标检测跟踪理论研究

项目编号： No.61273001

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 赵温波

作者单位： 中国人民解放军陆军军官学院

项目金额： 60万元

中文摘要： 情报雷达组网杂波/虚警范围广、强度高、目标跟踪难度大，现代隐身飞机/无人机等目标RCS越来越小，在组网杂波/虚警干扰下更难以检测跟踪。对于组网杂波/虚警目标和隐身飞机/无人机目标（简称"组网弱信噪比目标"），使用传统的联合概率数据关联算法和多假设算法，计算复杂度高，容易出现"组合爆炸"现象。本项目拟使用基于有限集统计(FISST)的一阶矩（PHD）方法研究解决组网弱信噪比目标检测跟踪问题。重点针对PHD使用卡尔曼"虚拟"观测模型不完整、PHD强度权重建模缺乏最优、PHD估计的配对点迹难以辨识等问题进行理论研究，分别采用全微分结合统计学理论、最优化理论、多叉树结合数据相关技术予以解决；此外，为提高PHD检测跟踪精度，拟采用垂线交叉、拟牛顿迭代等方法，分别实现多雷达在线误差校正、多雷达PHD联合定位等关键技术。通过研究，期望解决组网弱信噪比目标检测跟踪难题，丰富FISST的检测跟踪理论。

中文关键词： 雷达组网；弱信噪比；检测跟踪；概率假设密度；卡尔曼滤波

英文摘要： It was difficult to track the targets in wide-scope & high-intensity clutter & false-alarm (CFA) areas for intelligence radar networking system (RNS). Modern stealth aircrafts (SA) and unmanned air vehicles(UAV), with RCS getting smaller and smaller, were hardly detected & tracked in CFA interference environment of RNS. For tracking the targets of CFA and SA & UAV (The two kinds of target are called the low SNR target of radar networking, abbreviated as LSTRN), using two typical tracking algorithms, i.e., joint probability data association algorithm and multi-hypothesis tracking algorithm , required the computation of high complexity and could lean to the tendency of combination explosion. In this project, probability hypothesis density (PHD) theory, i.e., first-order moment of finite set statistics (FISST), will be used to detecting & tracking the LSTRN. On emphasis,for imcompleteness of virtual-measure modeling of PHD Kalman filter,non-optimization of weight-modeling of PHD intensity function, difficult identification of estimation-plot of PHD, this project will adopt total differential analysis combined with statictics theory,optimization theory, multi-way tree combined with data correlation technology for respective solution. In addition, in order to enhance precision of detecting & tracking of PHD,the pendu

英文关键词： radar networking；weak signal to noise ratio；detect and track；probability hypothesis density；Kalman filter