基于灰色理论的钟差预测模型辅助RAIM算法研究

Research on RAIM augmented with clock bias prediction model based on grey theory

为了提高钟差预测模型辅助接收机自主完好性监测(RAIM)的效果,提出了基于灰色理论的钟差预测方法。该方法针对钟差的一阶差分序列建立灰色模型,然后将基于该模型的钟差预测值引入卫星导航系统辅助进行故障卫星的检测与识别。试验表明,与传统的多项式模型相比,新的钟差模型不仅预测精度高;而且有效提高了故障卫星检测与识别的效率。在4颗卫星的条件下可检测的伪距故障偏差从64m降到54m,在5颗卫星的条件下可以识别的伪距故障偏差从55m降到46m,同时系统的其他指标也有明显改善。

To augment the performance of receiver autonomous integrity monitoring（RAIM） augmented with clock bias prediction model,a new method for predicting receiver clock bias based on grey theory is proposed in this paper.The first difference series of clock bias were used to construct the grey model,and then this model is used to augment RAIM algorithm in order to detect and identify faulty satellite.Experimental results show that the novel method not only has better predictive power for clock bias sequence,but also improves the detection and identification efficiency of faulty satellite and other performances obviously,compared with traditional polynomial models.The fault that can be identified decreases from 64 meters to 54 meters under four satellites,and the fault that can be identified decreases from 55 meters to 46 meters under five satellites.