摘要
为改善海平面高度反演过程中信号分离不佳以及偏差较大的问题,提出一种“高斯多峰拟合+滑动窗口”的海平面高度反演新方法。选取MAYG站BDS(Beidou Navigation Satellite System)卫星B2、B6及B7波段的信噪比(signaltonoiseratio,SNR),使用“高斯多峰拟合+滑动窗口”的方法对海平面高度进行反演,同多项式与小波滤波方法反演结果及验潮站数据进行对比验证。研究结果表明:高斯拟合反演结果精度相对于多项式和小波滤波方法反演精度分别提升29%和19%,且相关关系更优;结合滑动窗口后,反演结果有效点提升至加窗前的4.5倍,时间分辨率大幅提升;相较于单频数据反演结果,联合多频数据的反演结果有效点提升3倍以上,在保证精度的同时分辨率得到进一步提升;高斯拟合与滑动窗口相结合的GNSS-R(globalnavigation satellite system-reflectometry)海平面高度反演方法可更好地反映海平面高度的变化情况。
In order to improve the problem of poor signal separation and large deviation in the process of sea level height inversion,this paper proposes a new method of sea level height inversion based on Gaussian multi-peak fitting+sliding window,which can obtain high-quality signal-to-noise ratio sequence.The SNR(signal to noise ratio)of BDS(Beidou Navigation Satellite System)satellite B2,B6 and B7 bands at MAYG station was selected to invert the sea level height by Gaussian multi-peak fitting+sliding window method,and the inversion results of polynomial and wavelet filtering methods and tide station data were compared and verified.The results show that the accuracy of Gaussian fitting inversion results is 29%and 19%higher than that of polynomial and wavelet filtering methods,respectively,and the correlation is better.After combining the sliding window,the effective point of the inversion result is increased to 4.5 times before the windowing,and the time resolution is greatly improved.Compared with the single-frequency data inversion results,the effective point of the joint multi-frequency data inversion results is increased by more than 3 times,and the resolution is further improved while ensuring the accuracy.The results show that the GNSS-R(global navigation satellite system-reflectometry)sea level height inversion method combining Gaussian fitting and sliding window can better reflect the sea level height.
作者
王井利
周秀一
李如仁
王继野
WANG Jingli;ZHOU Xiuyi;LI Ruren;WANG Jiye(School of Traffic Engineering,Shenyang Jianzhu University,Shenyang 110168,China;China Railway 19th Bureau Group Mining Investment Company Limited,Beijing 100161,China)
出处
《辽宁工程技术大学学报(自然科学版)》
北大核心
2023年第5期555-563,共9页
Journal of Liaoning Technical University (Natural Science)
基金
国家自然科学基金项目(51774204)。