耦合PSO与扩展RBF神经网络估计NWM模型ZTD计算精度

doi:10.11947/j.AGCS.2022.20210117

测绘学报 ›› 2022, Vol. 51 ›› Issue (9): 1911-1919.doi: 10.11947/j.AGCS.2022.20210117

耦合PSO与扩展RBF神经网络估计NWM模型ZTD计算精度

张爽^1,2, 陈西宏¹, 刘强¹, 刘赞^1,3, 王庆力¹

1. 空军工程大学防空反导学院, 陕西西安 710051;
2. 93305部队, 辽宁沈阳 110000;
3. 93567部队, 河北保定 074100

收稿日期:2021-03-15 修回日期:2022-03-30 发布日期:2022-09-29
通讯作者: 刘强 E-mail:dreamlq@163.com
作者简介:张爽(1990—),男,博士生,研究方向为对流层散射与时间同步。E-mail:zhangbai0826@163.com
基金资助:
国家自然科学基金（61701525）

Estimating the ZTD accuracy of NWM model with PSO and extended RBF neural network

ZHANG Shuang^1,2, CHEN Xihong¹, LIU Qiang¹, LIU Zan^1,3, WANG Qingli¹

1. Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China;
2. Troops 93305, Shenyang 110000, China;
3. Troops 93567, Baoding 074100, China

Received:2021-03-15 Revised:2022-03-30 Published:2022-09-29
Supported by:
The National Natural Science Foundation of China (No. 61701525)

摘要/Abstract

摘要： 针对基于数值气象模型获取的对流层天顶延迟精度估计依赖外部基准的问题，本文构建耦合了粒子群算法与扩展径向基函数神经网络的ZTD精度估计模型，模型样本特征集利用NWM自身气象数据和地形特征数据构建，目标集以GNSS ZTD产品为参考值构建，模型规模结构通过层次聚类和模糊C均值聚类确定，模型参数通过粒子群算法优化。以欧洲中期天气预报中心提供的ERA5气压分层产品为NWM特例进行了模型训练和结果验证。结果表明，模型估计精度和泛化能力较好，平均估计精度优于4 mm，可在任意位置实现不依赖于外部参考基准的ZTD精度估计。

关键词: 对流层天顶延迟, 精度估计, 粒子群算法, 径向基神经网络, 数值气象模型

Abstract: To solve the problem that the accurate estimation of the zenith tropospheric delay (ZTD) obtained from the numerical weather model (NWM) depends on external benchmarks, a ZTD accuracy estimation model coupled with particle swarm algorithm and extended RBF neural network is constructed. The model sample is built using NWM's meteorological and terrain feature data. The target set is constructed using GNSS ZTD products as reference values. The model scale structure is determined by hierarchical clustering and fuzzy C-mean clustering, and the particle swarm algorithm optimizes the model parameters. The ERA5 pressure stratification product provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to train the model and verify the results for the NWM particular case. The results show that the model has good estimation accuracy and generalization capability. The average estimation accuracy is better than 4 mm and can achieve ZTD accuracy estimation at any location without relying on an external reference frame.

Key words: zenith tropospheric delay, accuracy estimation, particle swarm algorithm, radial-based neural network, numerical weather mode

中图分类号:

P207

张爽, 陈西宏, 刘强, 刘赞, 王庆力. 耦合PSO与扩展RBF神经网络估计NWM模型ZTD计算精度[J]. 测绘学报, 2022, 51(9): 1911-1919.

ZHANG Shuang, CHEN Xihong, LIU Qiang, LIU Zan, WANG Qingli. Estimating the ZTD accuracy of NWM model with PSO and extended RBF neural network[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9): 1911-1919.

参考文献

[1] 姚宜斌, 张顺, 孔建. GNSS空间环境学研究进展和展望[J]. 测绘学报, 2017, 46(10):1408-1420. DOI:10.11947/j.AGCS.2017.20170333. YAO Yibin, ZHANG Shun, KONG Jian. Research progress and prospect of GNSS space environment science[J]. Acta Geodaetica et Cartographica Sinica, 2017,46(10):1408-1420. DOI:10.11947/j.AGCS.2017.20170333.
[2] ZHENG F, LOU Y, GU S, et al. Modeling tropospheric wet delays with national GNSS reference network in China for BeiDou precise point positioning[J]. Journal of Geodesy, 2018, 92(5):545-560. DOI:10.1007/s00190-017-1080-4.
[3] FAN Haopeng, SUN Zhongmiao, ZHANG Liping, et al. A two-step estimation method of troposphere delay with consideration of mapping function errors[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(1):76-84.
[4] SUN Z, ZHANG B, YAO Y. An ERA5-based model for estimating tropospheric delay and weighted mean temperature over China with improved spatiotemporal resolutions[J]. Earth and Space Science, 2019, 6(10):1926-1941. DOI:10.1029/2019EA000701.
[5] SUN Z, ZHANG B, YAO Y. A global model for estimating tropospheric delay and weighted mean temperature developed with atmospheric reanalysis data from 1979 to 2017[J]. Remote Sensing, 2019, 11(16):1893-1914. DOI:10.3390/rs11161893.
[6] YAO Y, XU C, SHI J, et al. ITG:a new global GNSS tropospheric correction model[J]. Scientific reports, 2015,5:10273. DOI:10.1038/srep10273.
[7] BÖHM J, MÖLLER G, SCHINDELEGGER M, et al. Development of an improved empirical model for slant delays in the troposphere (GPT2w)[J]. GPS Solutions, 2015, 19(3):433-441. DOI:10.1007/s10291-014-0403-7.
[8] JIANG C, XU T, WANG S, et al. Evaluation of zenith tropospheric delay derived from ERA5 data over China using GNSS observations[J]. Remote Sensing, 2020, 12(4):663-682. DOI:10.3390/rs12040663.
[9] LI J, ZHANG B, YAO Y, et al. A refined regional model for estimating pressure, temperature, and water vapor pressure for geodetic applications in China[J]. Remote Sensing, 2020, 12(11):1713-1723. DOI:10.3390/rs12111713.
[10] XU C, YAO Y, SHI J, et al. Development of global tropospheric empirical correction model with high temporal resolution[J]. Remote Sensing, 2020, 12(4):721. DOI:10.3390/rs12040721.
[11] Cy46r1. IFS DOCUMENTATION PART I:OBSERVATIONS[EB/OL].[2020-11-27]. https://www.ecmwf.int/node/19745.
[12] ANDREI C O, CHEN R. Assessment of time-series of troposphere zenith delays derived from the global data assimilation system numerical weather model[J]. GPS Solutions, 2009, 13(2):109-117. DOI:10.1007/s10291-008-0104-1.
[13] CHEN Q, SONG S, HEISE S, et al. Assessment of ZTD derived from ECMWF/NCEP data with GPS ZTD over China[J]. GPS Solutions, 2011, 15:415-425. DOI:10.1007/s10291-010-0200-x.
[14] 黄瑾芳, 楼益栋, 张卫星, 等. 再分析资料计算中国区域对流层延迟精度[J]. 测绘科学, 2018, 43(5):13-17. HUANG Jinfang, LOU Yidong, ZHANG Weixing, et al. Analysis of the ZTD calculated data from China[J]. Science of Surveying and Mapping, 2018,43(5):13-17.
[15] 毛健, 崔铁军, 李晓丽, 等. 融合大气数值模式的高精度对流层天顶延迟计算方法[J]. 测绘学报, 2019, 48(7):862-870. DOI:10.11947/j.AGCS. 2019.20190003. MAO Jian, CUI Tiejun, LI Xiaoli, et al. A hight-accuracy method for tropospheric zenith delay error correction by fusing atmospheric numerical models[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(7):862-870. DOI:10.11947/j.AGCS. 2019.20190003.
[16] Copernicus Climate Change Service. ERA5 monthly averaged data on pressure levels from 1950 to 1978(preliminary version)[EB/OL].[2021-03-04]. https://cds.climate.copernicus.eu/cdsapp/dataset/reanalysis-era5-pressurelevels?tab=eqc.
[17] CMONOC. CMONOC基准站数据处理说明[EB/OL].[2021-05-04]. ftp://ftp.cgps.ac.cn/products, 2020.08. CMONOC. CMONOC Reference station data processing instructions[EB/OL].[2021-05-04]. ftp://ftp.cgps.ac.cn/products.
[18] LANDSKRON D, BÖHM J. VMF3/GPT3:refined discrete and empirical troposphere mapping functions[J]. Journal of Geodesy, 2018, 92(4):349-360. DOI:10.1007/s00190-017-1066-2.
[19] BOEHM J, HEINKELMANN R, SCHUH H. Short note:a global model of pressure and temperature for geodetic applications[J]. Journal of Geodesy, 2007, 81(10):679-683. DOI:10.1007/s00190-007-0135-3.
[20] LU YINGWEI N S. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm[J]. IEEE Transactions on Neural Networks, 1998, 9(2):308-318.
[21] 张伟, 黄卫民. 基于SAPSO算法的RBF神经网络设计[J]. 控制与决策, 2021, 36(9):2305-2312. ZHANG Wei, HUANG Weimin. Design of RBF neural network based on SAPSO algorithm[J]. Control and Decision, 2021, 36(9):2305-2312.
[22] HUANG G B, SARATCHANDRAN P, SUNDARARAJAN N. An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2004, 34(6):2284-2292. DOI:10.1109/tsmcb.2004.834428.
[23] 翟莹莹, 左丽, 张恩德. 基于参数优化的RBF神经网络结构设计算法[J]. 东北大学学报(自然科学版), 2020, 41(2):176-181, 187. ZHAI Yingying, ZUO Li, ZHANG Ende. Algorithm for structure design of RBF neural network based on parameter optimization[J]. Journal of Northeastern University (Natural Science), 2020, 41(2):176-181, 187.
[24] 梁泽, 王玥瑶, 岳远紊, 等. 耦合遗传算法与RBF神经网络的PM_2.5浓度预测模型[J]. 中国环境科学, 2020, 40(2):523-529. LIANG Ze, WANG Yueyao, YUE Yuanwen, et al. A coupling model of genetic algorithm and RBF neural network for the prediction of PM_2.5 concentration[J]. China Environmental Science, 2020, 40(2):523-529.
[25] 陈黎飞, 姜青山, 王声瑞. 基于层次划分的最佳聚类数确定方法[J]. 软件学报, 2008,19(1):62-72. CHEN Lifei, JIANG Qingshan, WANG Shengrui. A hierarchical method for determining the number of clusters[J]. Journal of Software, 2008,19(1):62-72.

耦合PSO与扩展RBF神经网络估计NWM模型ZTD计算精度

Estimating the ZTD accuracy of NWM model with PSO and extended RBF neural network

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	颜金彪, 吴波, 何清华. 顾及各向异性的多参数协同优化IDW插值方法[J]. 测绘学报, 2021, 50(5): 675-684.
[2]	毛健, 崔铁军, 李晓丽, 陈莉, 孙艳玲, 高爽, 张辉. 融合大气数值模式的高精度对流层天顶延迟计算方法[J]. 测绘学报, 2019, 48(7): 862-870.
[3]	陈应霞, 陈艳, 刘丛. 遥感影像融合AIHS转换与粒子群优化算法[J]. 测绘学报, 2019, 48(10): 1296-1304.
[4]	赵建虎, 王晓, 张红梅, 胡俊, 简晓敏. 侧扫声呐图像分割的中性集合与量子粒子群算法[J]. 测绘学报, 2016, 45(8): 935-942.
[5]	方兴, 曾文宪, 刘经南, 姚宜斌, 王勇. 基于非线性高斯-赫尔默特模型的混合整体最小二乘估计[J]. 测绘学报, 2016, 45(3): 291-296.
[6]	詹银虎, 郑勇, 张超, 骆亚波, 王凯. 鱼眼相机矢量观测检校模型及其应用[J]. 测绘学报, 2016, 45(3): 332-338.
[7]	谭兴龙, 王坚, 赵长胜. 神经网络辅助的GPS/INS组合导航自适应UKF算法[J]. 测绘学报, 2015, 44(4): 384-391.
[8]	黄良珂刘立龙文鸿雁姚朝龙. 亚洲地区EGNOS天顶对流层延迟模型单站修正与精度分析[J]. 测绘学报, 2014, 43(8): 808-817.
[9]	谢顺平,冯学智,都金康. 基于网络Voronoi图启发式和群智能的最大覆盖空间优化[J]. 测绘学报, 2011, 40(6): 778-784.
[10]	王家耀，谢明霞，郭建忠，陈科. 基于相似性保持和特征变换的高维数据聚类改进算法[J]. 测绘学报, 2011, 40(3): 269-275.