基于截获信号多特征融合跟踪的三星定位系统辐射源认知方法研究

项目名称： 基于截获信号多特征融合跟踪的三星定位系统辐射源认知方法研究

项目编号： No.61201304

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 李宏博

作者单位： 哈尔滨工业大学

项目金额： 24万元

中文摘要： 随着雷达辐射源复杂波形技术的发展和多工作模式的应用，信号的多变性增加了对辐射源认知的难度。而三星无源定位系统空间分辨力较低、量测误差统计特性不确定、目标处于探测区域的时间有限且重访周期较长，难以对辐射源信息连续跟踪。本项目研究基于目标位置和信号形式多特征融合跟踪的辐射源认知方法，识别、分析并掌握辐射源的工作模式和活动规律，力图在提高辐射源认知处理的连续性、完整性和一致性方面有所创新。在目标运动跟踪的基础上，引入多项信号特征，对来源于同一辐射源的三星截获信号特征进行周期式时域融合与分布式空域融合跟踪。构造辐射源位置和信号特征参数联合向量的多维关联算法，并根据辐射源所处状态自适应调整。建立辐射源跟踪续断处理准则，在缺失观测的情况下应用自适应盲跟踪处理，探索将辐射源的多种工作模式归并的认知方法，降低目标序列冗余。项目成果对于提高电子侦察系统情报保障水平、服务战略决策具有十分重要的意义。

中文关键词： 三星定位系统；辐射源认知；辐射源跟踪；航迹处理；多特征融合

英文摘要： With the development of complex waveforms technique for radar emitter and the application of multi-mode, the difficulty of emitter cognition is increased because of signal variability. Moreover, in Tri-satellite passive location system, spatial resolution is Low, the statistical characteristics of measurement error is uncertain, the time for the target in the detection region is limited and revisit period is long. Therefore, it is difficult to tracking emitter information continuously. In this project, the emitter cognition algorithm is investigated based on multi-feature fused tracking of target location and signal forms, in order to identify, analysis, master the patterns and activity order of emitter, and try to make innovation in improving the continuity, integrity and consistency of emitter cognition. Importing multi-feature of signal to target motion tracking processing, the intercepted signal features from the same emitter to three satellites are fused tracking periodically in time domain and distributed in space domain. The multi-dimensional correlation algorithm is constructed with the joint vector of emitter position and signal features, and is adapted according to the emitter state. The break-continue tracking rules are established applying adaptive blind tracking in the case of observations missing.

英文关键词： Tri-satellite Location System；Emitter Cognition；Emitter Tracking；Trace Processing；Multi-feature Fusion