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作 者:刘硕[1,2] 张磊[1,2] 李健[1,2] LIU Shuo ZHANG Lei LI Jian(School of Information and Electronics, Beijing Institute of Technology, Beijing 100081,China Key Laboratory of Electronic and Information Technology in Satellite Navigation, Beijing Institute of Technology, Beijing 100081 ,China)
机构地区:[1]北京理工大学信息与电子学院,北京100081 [2]北京理工大学卫星导航电子信息技术教育部重点实验室,北京100081
出 处:《武汉大学学报(信息科学版)》2017年第7期1015-1020,共6页Geomatics and Information Science of Wuhan University
基 金:国家自然科学基金(61101128);国家863计划(2013AA122103)~~
摘 要:GNSS(global navigation satellite systems)系统时使用原子钟作为时间基准,相比使用晶振的GNSS接收机,其稳定度高出几个量级。GNSS系统间钟差相比接收机钟差具有更高的稳定度,如果可以充分利用此先验信息将有助于优化多GNSS系统的定位结果。分析如何充分利用系统间钟差更稳定这一先验信息,并测试引入这一先验信息对多系统单点定位结果的影响,推导了基于两种不同钟差估计方法的定位解算模型,给出了最小二乘和扩展卡尔曼滤波两种参数估计算法。通过比较不同模型和估计算法在静态和动态定位的实验结果,最小二乘法无法利用系统间钟差更稳定的特点改善定位精度;静态实验结果表明,扩展卡尔曼滤波自身有一定的降噪效果,若引入系统间钟差更稳定的先验信息,将更有利于减小噪声;动态实验结果表明,引入系统间钟差更稳定的先验信息可减弱扩展卡尔曼滤波的发散现象。GNSS(global navigation satellite systems)time uses atomic clock as the time standard while GNSS receiver uses crystal oscillator.The stability of atomic clock is several times of magnitudes better than crystal oscillator.Therefore,the intersystem time offset between different GNSS is more stable than receiver clock bias.It will benefit the positioning results if we can fully use that prior information.The purpose of this contribution is to analyze how to use that prior information properly and examine the influence on it.We deduce two positioning model based on different clock bias estimation method and introduce two parameter estimation algorithm:least square(LS)and extended Kalman filter(EKF).Static and dynamic tests were carried out and some conclusions are made:the LS cannot improve the positioning performance with the prior information of that the intersystem time offset between different GNSS is more stable than receiver clock bias.Static test results show the EKF itself has some kind of noise suppression.The noise is less in case we use the"stable"prior information.Dynamic test shows divergence of EKF can be suppressed by using the"stable"prior information.
分 类 号:P228.41[天文地球—大地测量学与测量工程]
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