城市环境下惯性辅助的GNSS多粗差探测方法

Inertial-Aided GNSS Multiple Gross Errors Detection Method in Urban Environment

当多星座的GNSS接收机存在多个粗差时,传统的粗差检测和排除(FDE)方法需要通过频繁的搜索来排除潜在的观测值,剔除粗差的效率低,且定位性能可能急剧降低。提出一种惯性辅助的多粗差检测与排除方法,将惯性状态模型和测量模型的信息引入到自主完好性监测中,来构造全局和局部检验统计量检测粗差,并有效地排除错误测量值。结果表明,对于接收机存在2个以上粗差时,传统的FDE方法误检和漏检率高,而本文方法可显著提高检测效率,提高定位精度。

When multiple gross errors exist in a multi-constellation GNSS receiver,the traditional gross error detection and exclusion(FDE)method requires frequent searching to exclude potential observations,and the efficiency of excluding gross errors is low and positioning accuracy may be drastically reduced.Therefore,we propose an inertial-aided multiple gross errors detection and exclusion method.This method introduces the inertial state model and measurement model into the receiver autonomous integrity monitoring to construct the global and local test statistic to detect the outliers,excluding the wrong observations.The results show that when there are more than two gross errors in the receiver,the traditional gross error detection and exclusion method has a high rate of false detection and missing detection,and our method can significantly improve the detection efficiency and positioning accuracy.