| 引用本文: | 贺小星,花向红,鲁铁定,等.时间跨度对GPS坐标序列噪声模型及速度估计影响分析.[J].国防科技大学学报,2017,39(6):12-18.[点击复制] |
| HE Xiaoxing,HUA Xianghong,LU Tieding,et al.Effect of time span on GPS time series noise model and velocity estimation[J].Journal of National University of Defense Technology,2017,39(6):12-18[点击复制] |
|
| |
|
|
| 本文已被：浏览 8352次 下载 6854次 |
| 时间跨度对GPS坐标序列噪声模型及速度估计影响分析 |
| 贺小星1,2, 花向红1, 鲁铁定3, 余科根1, 宣伟1 |
|
(1. 武汉大学 测绘学院， 湖北 武汉 430079;2. 华东交通大学 土木建筑学院， 江西 南昌 330013;3. 东华理工大学 测绘工程学院， 江西 南昌 330013)
|
| 摘要: |
| 选取全球范围内72个基准站的坐标序列，采用改进的赤池信息量准则、贝叶斯信息量准则对不同噪声模型组合进行噪声分析，得到基准站坐标序列的最优噪声模型及速度参数，探讨时间序列跨度对噪声模型及速度估计的影响。结果表明，基准站坐标序列噪声模型不能由单一的噪声模型表述，其呈现出多样性特征，主要表现为幂律噪声、高斯马尔科夫噪声、闪烁噪声+白噪声特征，且三坐标分量表现出不同的噪声特性；随着时间跨度的增加，坐标时间序列的最优噪声模型、GPS站速度及其不确定度逐渐由发散趋于收敛，随机游走噪声模型的比重有所增加。结果表明10 a以上的时间跨度是较为理想的噪声模型估计尺度。 |
| 关键词: 时间序列分析 噪声模型估计 速度不确定性 赤池信息量准则 贝叶斯信息量准则 |
| DOI：10.11887/j.cn.201706003 |
| 投稿日期：2016-08-05 |
| 基金项目:国家自然科学基金资助项目（41464001，41674005，41504025）；江西省数字国土重点实验室开放研究基金资助项目（DLLJ201701）；江西省自然科学基金资助项目（20171BAB203032，20161BAB213087）；江西省教育厅科学技术研究资助项目（GJJ150523） |
|
| Effect of time span on GPS time series noise model and velocity estimation |
| HE Xiaoxing1,2, HUA Xianghong1, LU Tieding3, YU Kegen1, XUAN Wei1 |
|
(1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;2. School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013, China;3. Faculty of Geomatics, East China University of Technology, Nanchang 330013, China)
|
| Abstract: |
| The time series of 72 international GPS service core stations were selected to make perform analysis on GPS noise model and uncertainties of the velocity. An improved Akaike information criteria and a Bayesian information criteria model were developed to evaluate different combinations of noise model, so as to establish the best noise model of GPS time series and gain the aureate velocity parameters. Results show that the noise model of GPS time series cannot be described as simple noise, it tends to showing diversity, and can be best described by power-law, generalized Gauss Markov, flicker noise plus white noise model, and its three components exhibit different noise characteristics. With the time span increases, the optimal noise model of time series, GPS station velocity and its uncertainty tends to be convergent and steady. Besides, the proportion of random walk noise is proved to increase as time span increases. The final results show that the time span of more than 10 years is an ideal noise model estimation scale. |
| Keywords: time series analysis noise model estimation velocity uncertainty Akaike information criteria Bayesian information criteria |
|
|
|
|
|
