摘要
针对标准Kalman滤波算法在GNSS定位的应用中还存在精确性不高的问题。本文提出了一种基于模糊度参数优化Kalman算法的GNSS智慧城管数据采集定位模型。在整周模糊度未知的情况下,可将双差整周模糊度作为状态向量的一部分,同时利用伪距、载波相位双差观测值,在滤波估计动态接收机天线位置以及速度的同时,估计双差载波相位整周模糊度。算法仿真结果表明,本文提出的基于模糊度参数优化的Kalman算法相比较标准Kalman算法,在智慧城管数据采集定位的应用中,具有更高的精确性。
According to the low accuracy of standard Kalman filtering algorithm in GNSS positioning applications, this paper proposes a data acquisition positioning model for GNSS Smart City Administration based on Kalman algorithm with optimized ambiguity parameter. Under the unknown condition of the whole week ambiguity, double difference ambiguity of the whole week as part of the state vector. At the same time using pseudo-range and carrier phase double difference observation, and in the filter to estimate the dynamic receiver antenna position and speed, estimate the double difference carrier phase ambiguity of the whole week. Algorithm simulation results show that, compared with the standard Kalman algorithm, the proposed Kalman algorithm based on optimized ambiguity parameter has the higher accuracy in application of data acquisition positioning in Smart City Administration.
出处
《科技通报》
北大核心
2016年第1期179-182,共4页
Bulletin of Science and Technology
基金
山东省高等学校科研计划项目(No.J15LN74)
关键词
北斗GNSS
智慧城管
数据采集
定位优化
Beidou GNSS
smart city administration
data acquisition
location optimization
