Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and...Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.展开更多
利用IGS提供的双频GNSS观测数据,分析了 Kalman方法解算电离层垂直总电子含量(Vertical Total Electron Content,VTEC)存在的问题,提出了 Kriging-K alman改进解算方法,并对两种方法解算的电离层VTEC进行分析和比较.结果表明:在低纬地区...利用IGS提供的双频GNSS观测数据,分析了 Kalman方法解算电离层垂直总电子含量(Vertical Total Electron Content,VTEC)存在的问题,提出了 Kriging-K alman改进解算方法,并对两种方法解算的电离层VTEC进行分析和比较.结果表明:在低纬地区,当观测卫星数量发生改变时,Kalman方法解算的VTEC存在跳变异常,Kriging-K alman方法解算的VTEC变化较为平稳,不存在跳变现象.对比分析耀斑期间两种方法解算VTEC的变化,发现Kalman方法解算的VTEC变化明显小于耀斑引起VTEC的增量;Kriging-K alman方法解算结果与实际变化相一致.表明Kriging-Kalman方法计算精度更高,能够更精确计算耀斑等剧烈异常空间天气活动期间的VTEC及其变化,有利于电离层VTEC日常精确监测、研究和工程应用.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant No.2016YFA0302101)the Initiative Program of State Key Laboratory of Precision Measurement Technology and Instrument。
文摘Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.
文摘利用全球导航卫星系统(Global Navigation Satellite System,GNSS)双频差分信号进行电离层电子含量反演是一种常用的电离层探测手段,但GNSS信号在强电磁干扰环境下,被淹没于电磁噪声中而无法被提取,影响电离层总电子含量(total electron content,TEC)反演系统的可靠性。采用传统调零抗干扰阵列天线方案能解决干扰源剥离的问题,但调零信号的天线相位中心不稳定导致高精度的相位平滑伪距和精密单点定位(precise point positioning,PPP)算法无法收敛。针对强干扰环境下的电离层监测需求,本文提出一种抗干扰TEC数据反演手段,通过对阵列天线通道幅相一致性进行校正,保证相位中心的稳定性,从而推算出准确的电离层TEC信息,提高了系统的可靠性和抗干扰能力。
文摘利用IGS提供的双频GNSS观测数据,分析了 Kalman方法解算电离层垂直总电子含量(Vertical Total Electron Content,VTEC)存在的问题,提出了 Kriging-K alman改进解算方法,并对两种方法解算的电离层VTEC进行分析和比较.结果表明:在低纬地区,当观测卫星数量发生改变时,Kalman方法解算的VTEC存在跳变异常,Kriging-K alman方法解算的VTEC变化较为平稳,不存在跳变现象.对比分析耀斑期间两种方法解算VTEC的变化,发现Kalman方法解算的VTEC变化明显小于耀斑引起VTEC的增量;Kriging-K alman方法解算结果与实际变化相一致.表明Kriging-Kalman方法计算精度更高,能够更精确计算耀斑等剧烈异常空间天气活动期间的VTEC及其变化,有利于电离层VTEC日常精确监测、研究和工程应用.
