卡尔曼滤波算法的GPS双差观测值周跳探测与修复

Cycle-slip detection and correction for GPS double difference observations based on Kalman filter

针对非差相位观测值中误差来源较多,对周跳探测不够敏感等问题,双差观测值可大幅削弱相关性较强的误差,较好地探测序列中小的周跳。该文以GPS双差观测序列为研究对象,运用卡尔曼滤波算法,建立双差观测序列周跳探测与修复模型。最后通过理论和实验分析,验证了模型的有效性和可靠性。

Aiming at the to the detection of cycle slip, errors, and better detect the problem that there are many errors in the phase observation, and insensitive the double difference observation can significantly weaken the relative strong small cycle-slips of the sequence. Taking the GPS double differential observa- tion series as the research object, this paper uses the Kalman filter algorithm to establish the cycle-slip tection and correction model of GPS double difference series. Finally, the validity and reliability of model are verified by theoretical and experimental analysis.