基于随机森林的复合导引头跟踪模式决策

为了提高多模复合导引头跟踪模式复杂逻辑决策的适应性，采用随机森林构建模式决策网络、结合误差补偿的方法，对传统的人工条件判读流程进行了优化，提高了模型的分类准确率，并将该方法应用到复合导引头中进行理论分析和仿真验证。结果表明，得到的跟踪模式的分类正确率达100%，运行速度提升16%，代码量减少3倍; 基于随机森林和误差补偿的跟踪模式决策方法对导引头跟踪模式决策具有较好的适应性，可以高效实现跟踪模式的自主决策。此研究算法为高效率地实现导引头跟踪模式决策提供了参考。

Tracking mode decision of compound seeker based on random forest

In order to improve the adaptability of complex logic decision-making in the tracking mode of the compound seeker, a mode decision network was constructed using random forest. The model error compensation was combined to optimize the traditional manual condition interpretation process, and the classification accuracy of the model was improved at the same time. The proposed method was added to the compound seeker for theoretical analysis and simulation verification. The results show that the classification accuracy of tracking mode is about 100%, and the running speed increased by 16%, the amount of code was reduced by 3 times. The optimized tracking mode decision process, which based on random forest and error compensation, can good adapt to the seeker tracking mode decision, efficiently implement the autonomous decision of tracking mode. This research algorithm provides a reference for the efficient realization of seeker tracking mode decision-making.