Algorithm On The Multi-target Passive Tracking With Data Fusion System

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.