基于PSO-BP神经网络的广播星历轨道误差预测模型

在卫星导航数据处理实践中,发现广播星历轨道误差中客观存在不确定性的规律现象,针对这种不能用确定数学模型表示的误差信息,建立基于粒子群优化反向传播(back propagation,BP)神经网络的轨道误差预测模型。通过粒子群算法对BP神经网络的初始权值和阈值进行全局寻优,利用广播星历解算出的卫星空间位置和速度,并结合时间信息和摄动改正数对神经网络进行训练和测试。结果表明该模型对广播星历轨道误差具有较好的拟合能力和预测效果,用该模型对卫星位置解算提供误差补偿,可有效提高卫星定轨精度,降低系统级误差。

Prediction model of broadcast ephemeris orbit error based on PSO-BP neural network

In the practice of satellite navigation data processing, it is found that there is uncertainty and regularity in the broadcast ephemeris orbit error. For the reason that this kind of error information cannot be represented by a definite mathematical model, an error prediction model based on the particle swarm optimization back propagation (BP) neural network is established. In this model, the particle swarm optimization (PSO) is used to globally optimize the initial weights and thresholds of the BP neural network. The satellite position and velocity, calculated by broadcast ephemeris, with time information and perturbation correction parameters, are combined together to train and test the neural network. The results show that model’s fitting ability and prediction effect to the broadcast ephemeris orbit error are better. This model can be used to provide error compensation for satellite position calculation, so the accuracy of satellite orbit determination can be improved effectively and the system-level error can be reduced.