基于径向基函数神经网络和无迹卡尔曼滤波的弹丸落点预报方法研究

为了能够在飞行数据不尽精确的情况下进行快速、准确的落点预报，提出一种基于径向基函数（RBF）神经网络和无迹卡尔曼滤波技术的弹丸落点预报方法。使用RBF神经网络逼近外弹道方程用以预报弹丸落点，并用改进型量子行为粒子群算法优化网络结构和权阈值，在此基础上对基于神经网络的初步预报数据进行滤波处理。最后进行预报仿真，在输入数据有噪声的情况下依然得到了较高的预报精度，从而证明该方法对预报弹丸落点是有效可行的，为弹丸的落点预报的实际应用提供了参考。

Projectile Impact-point Prediction Method Based on Radial Basis Function Neural Network and Unscented Kalman Filter

A new prediction method based on radial basis function (RBF) neural network and an unscented Kalman filter technology is proposed for the precise and quick prediction of impact-point without exact flight data. Firstly, RBF neural network approximated external ballistics equation is used to predict the projectile impact-point, and the improved quantum-behaved particle swarm optimization algorithm is used to optimize the training method. On this basis, the tentative prediction data is processed with unscented Kalman filter. At last, the prediction simulation is carried out. The results show that a high prediction precision can be reached under the condition of input data with noise. The method proposed in this paper is efficient and available for impact-point prediction.