【作者】 郭磊；

【导师】 唐斌； 刘刚；

【作者基本信息】 电子科技大学 ， 信息与通信工程， 2008， 博士

【摘要】 被动雷达是电子对抗系统中重要的组成部分，已成为各国未来武器系统中重要的发展方向之一。由于目标辐射源信号和电磁波传播环境的复杂性，导致被动雷达的跟踪精度较低。若利用数据融合方法有效地融合被动雷达的不同信息，从而改善目标跟踪精度，同时提高被动雷达对多目标的分辨率。本论文正是针对上述问题，以被动雷达数据融合算法为主要的研究对象，在深入分析影响目标跟踪的主要因素的基础上，对被动雷达数据关联、目标测量数据和目标状态融合算法作了创新性和探索性研究。本文的主要内容和创新如下：1．深入分析了影响被动跟踪的主要因素。将PCRLB(Posterior Cramer-RaoLower Bound)理论应用于单目标被动跟踪情况中，给出了仅使用角度跟踪PCRLB的数学表达式，在表达式中考虑了过程噪声等因素。另外，这一结果被进一步推广，给出了多目标情况下的PCRLB数学表达式。最后，通过仿真直观得给出了各因素与跟踪精度下限的关系。2．提出了被动跟踪条件下的联合概率数据关联(Join Probability DataAssociation JPDA)算法。利用被动雷达所提取的目标辐射源信号特性，对传统的JPDA算法进行了改进，提高了目标关联正确概率。在此基础上，提出了目标信号分类矩阵JPDA算法，并将该算法推广到多传感器多目标的JPDA算法中，提高被动雷达对多目标的分辨率。3．研究了传统的测量数据融合算法，在此基础上提出了针对被动跟踪的通用测量数据融合算法。另外，结合文中提出的多目标数据关联算法，给出了多目标通用测量数据融合算法。并将此通用算法应用在三个不同的被动测量数据中，通过仿真试验与传统的测量融合算法进行比较，该算法能有效改善目标跟踪精度。4．通过理论推导给出了基于协方差交叉状态融合的Kalman滤波算法。并将该算法与传统的状态融合算法进行了比较，新算法整体性能更优。另外，新算法与测量融合比较，在跟踪精度上接近，但计算量更小，通信量更少。同时，本文对影响算法的三个主要因素，即采样时间、系统是否反馈和传感器数量，进行了深入的分析。进一步，结合修正的K近邻域航迹关联算法，将该算法推广到多传感器多目标的情况，并给出了仿真试验证明其有效性。更多还原

【Abstract】 As important part of electromagnetic countermeasure system, the development and application of passive radar is one of primary development directions in the future weapon systems of various nations. Due to complexity of target’s radiation source signal and of electromagnetic wave parpagation environment, the tracking precision of passive radar is low. The study shows that data fusion technology can effectively fuse different information of passive radar, and thus it can improve the tracking precision and resolution of target.In this dissertation, passive radar data fusion algorithm is studied in detail. Based on the analysis of main factors affecting the precision of target tracking, the study on passive radar data correlation, target measurement data and target state fusion algorithm is done. Main work and breakthrough are presented as follows:1. The analysis of main factors affecting passive tracking is done. Then, Posterior Carmer-Rao Lower Bound (PCRLB) theory is applied in the passive tracking of single target, and the mathematical formula of the PCRLB only using angle is derived in which some factors such as process noise are considered. In addition, the mathematical formula of the PCRLB for multi-targets is also derived after generalization. Finally, simulation experiments are done to reveal the relation between different factors and the low bound of tracking precision for intuition.2. The Joint Probability Data Association (JPDA) algorithm is presented. By using the characteristics of target radiation signal extracted by passive radar, the improvement on the traditional JPDA algorithm is done, in which target correlation exact probability is further improved. Then, the target signal classification matrix JPDA algorithm is presented. Furthermore, it is generalized to include the case of multi-sensors multi-targets JPDA algorithm, which can improve the tracking resolution of passive radar tracking multi-targets.3. The traditional measurement data fusion algorithm is studied, on which the general measurement data date fusion algorithm is derived. In addition, combined with the multi-targets data correlation algorithm presented by the author, the multi-targets measurement data date fusion algorithm is presented. In order to verify the validity of the new algorithm, it is applied in the simulation experiments of three different passive measurements. The simulation results show that the new algorithm can effectively improve the target tracking precision in comparison with the traditional ones.4. The Kalman filtering algorithm based on the covariance cross state fusion is theoretically derive in this dissertation. The comparison between the new algorithm and the traditional state fusion algorithm shows that the performance of the new one is better than that of the latter. In addition, the comparison between the new algorithm and the traditional measurement fusion algorithm shows that the computation cost and communication traffic of the new one are lower than those of the latter when the tracking precision is kept same. At the same time, three main factors affecting the algorithm is studied in detail, i.e., whether system is feedback, sampling time and the number of sensors. Furthermore, combined with the modified K neighborhood flight track correlation algorithm, the new algorithm is generalized to include the case of multi-targets. Finally, the simulation experiments are done to verify its validity.更多还原