基于Bayes弹道导弹主动段融合跟踪算法

弹道导弹主动段拦截中对导弹跟踪精度要求很高，如何将多种主动段探测装备得到的数据进行融合得到更加精确的数据是当前亟待解决的难题。针对此问题，文中研究了导弹预警卫星与雷达融合跟踪，提出了一种基于Bayes理论的弹道导弹主动段融合跟踪算法。该算法分别建立了导弹预警卫星和雷达对主动段探测模型和跟踪模型，应用POFACETS软件仿真了一种类型弹道导弹，并将其获得的导弹RCS数据应用到算法中，提高了算法的准确性。仿真实验表明，该融合跟踪算法可以获得比多个传感器算术平均值更精确的结果，具有较高的可靠性。

Fusion tracking algorithm of boost-phase ballistic missile using Bayes

The demand of tracking accuracy is very high in the boost-phase ballistic missile interception, how to fuse the data of many kinds of boost-phase detection equipments to get more precise data has become an urgent problem. To solve this problem, a new fusion tracking algorithm based on missile early warning satellites and radar was studied, a fusion tracking algorithm of boost-phase based on Bayes theory was proposed. The detection model and tracing model of missile early warning satellite and radar for boost-phase were established, the accuracy of this fusion algorithm was improved by the help of POFACETS' RCS simulation of one type of ballistic missile. Simulation results show that the result is more accurate than by using arithmetic mean on the limited sensors, this algorithm has high reliability.