基于卡尔曼滤波的AR 模型无人自主空中加油预测制导策略

为了改善无人机自主空中加油(AAR)“捕获”阶段中受油机对锥套的“被动跟随”状况，提出一种基于卡尔曼滤波的AR模型无人自主空中加油预测制导策略。利用自回归(auto regressive，AR)模型预测锥套飘摆的未来位置作为受油机导航点，并依据卡尔曼(Kalman)滤波原理对模型进行参数估计，通过加油仿真试验现象对提出方案的实时性能进行定量分析，并提出4种提高实时性的优化措施。仿真结果表明：该算法具有极高的预测精度，改善后的方案满足空中加油场景的实时性要求，能够使加油对接成功率显著提高，对无人机自主空中加油技术的实现具有较重要的意义。

AAR Predictive Guidance Scheme with AR Model Based on Kalman Filter

To improve the passive tracking situation of receiver for drogue in AAR’s “capture” phase, predictive guidance scheme was implemented. With help of AR model, predicted drogue position was regarded as target point of receiver, and parameters of the model were estimated by Kalman filter principle. Phenomena throughout the AAR simulation experiment were analyzed quantitatively, and four optimization measures were proposed to improve real-time performance of the scheme. The simulation results indicate that proposed algorithm possesses high prediction precision and that optimized scheme significantly enhance success rate of AAR, which is of crucial importance in achievement of AAR technique.