摘要
针对当前接收机自主完备性监测(receiver autonomous integrity monitoring,RAIM)算法中存在多粗差探测识别能力较弱、计算效率不足等问题,提出一种基于Mean Shift(MS)模型的多粗差探测RAIM算法。首先利用QR奇偶检校法构建样本数据集和QR检验向量;其次基于MS模型估计样本密度中心,并以此作为MS检校向量,使用观测向量与MS检校向量的距离来评价观测值可靠程度,从而确定异常观测卫星;最后联合观测向量、MS和QR检校向量构建基于距离关系的权系数函数,对多个异常观测进行处理。实验结果表明,基于MS检校向量的粗差判别方法在多粗差存在的情况下,具有更灵敏的粗差识别能力;相比最小二乘残差法,新型RAIM算法改善了多粗差探测识别能力和计算效率,可有效提高多系统融合单点定位的可靠性。
There is no a good balance between the performance of the detection and the recognition and the calculation efficiency for multiple gross errors in the current receiver autonomous integrity monitoring(RAIM)algorithm.In this paper,the Mean Shift(MS)model is introduced to resolve these problems of the RAIM algorithm.Firstly,the QR parity check method is used to build a sample dataset and the QR calibration vector.Then,the density center is estimated by using the MS model,which is regarded as the MS calibration vector.The distance between the observation vector and the MS calibration vector can be applied for evaluating the reliability of global navigation satellite system(GNSS)observations,and determining the abnormal satellites.Finally,we use the weight coefficient function with a qualitative distance which is derived from the combination of the observation vector,the MS calibration vector and the QR calibration vector to select the abnormal observations and to furtherly promote the performance of detection and recognition of the calculation efficiency of multiple gross errors.The experimental results demonstrate that the gross error discrimination method based on the MS calibration vector has a more sensitive recognition ability in the presence of multiple gross errors.In addition,the new RAIM algorithm can not only obtain the better performance of detection and recognition and the calculation efficiency of multiple gross errors,but also can effectively improve the reliability of single point positioning with multi-system fusion,compared with the least square residual method.
作者
刘一
周威
金际航
边少锋
谷守周
LIU Yi;ZHOU Wei;JIN Jihang;BIAN Shaofeng;GU Shouzhou(College of Electrical Engineering,Naval University of Engineering,Wuhan 430033,China;Chinese Academic of Surveying and Mapping,Beijing 100830,China;Guangxi Key Laboratory of Spatial Information and Geomatics,Guilin 541004,China;Naval Institute of Hydrographic Surveying and Charting,Tianjin 300061,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2022年第2期644-650,共7页
Systems Engineering and Electronics
基金
国家自然科学基金(41631072,41971416,41876222)
国家重点研发计划(2016YFB0501801)
全球连续监测评估系统(GFZX0301040308-06)
湖北省杰出青年科学基金(2019CFA086)
广西空间信息与测绘重点实验室(19-050-11-02)资助课题。
关键词
接收机自主完好性监测
多粗差
均值漂移
多全球导航卫星系统
单点定位
receiver autonomous integrity monitoring(RAIM)
multiple gross errors
Mean Shift(MS)
multi-GNSS
single point positioning
