不确定航迹自适应预测模型

针对现有航迹预测技术中，存在无模技术缺乏理论分析支持、能力有限、适用范围窄、有模技术先验假设过多、前提条件严苛、通用性差等问题，通过理论推导，利用循环和多层神经网络结构，研究提出了具有理论严谨、先验假设少、适用范围广、通用性强等优点的不确定航迹自适应预测模型，同时给出了典型的实现方法。该模型克服了现有航迹预测方法的缺点与不足，并具有无模与有模两类技术的优点与长处，仿真和实测实验验证表明：该模型能很好地提取识别出数据中存在的模式，并基于模式，进行正确有效地预测，能有效解决不同实际环境中的航迹预测问题，效果明显。

Adaptive forecast model for uncertain track

There are two types track forecast technologies at present. One is model-free type technologies which have little theoretical support, limited capacity and narrow scope of application. The other is model type technologies which have too many prior hypothesis, strict prerequisite conditions and poor universality. Against above problems and to solve the track forecast problem effectively, an uncertain track adaptive forecast model and corresponding exemplary implementation are proposed based on the structures of recurrent neural network and the multi-layer neural network. The proposed model has rigorous theoretical support, less a priori hypotheses, wide application range and strong versatility, which inherits benefits and overcomes weaknesses of existing track forecast methods. Simulation and experimental results show that the proposed model can extract and recognize the patterns in the data set, and make correct and effective prediction according to the recognized pattern, significantly solving the track forecast problems in real environments.