Fitting improvement of PPP receiver tracking error stochastic model for high latitudes

doi:10.11947/j.AGCS.2024.20230323

Abstract

Abstract:

Ionospheric scintillation refers to the phenomenon of rapid random fluctuations in the amplitude and phase of radio signals which can result in increasing of measurement noise and signal loss of lock. At present, the receiver tracking error stochastic (RTES) model can effectively reduce the influence of ionospheric scintillation on GNSS precision point positioning (PPP). However, the RTES model relies on the data products from specialized ionospheric scintillation monitoring receivers (ISMR). Compared with geodetic receivers widely distributed around the world, the number of ISMR monitoring stations is very limited, and the acquisition of ISMR data products is difficult. Using the GPS observation and scintillation data recorded by the Canadian high arctic ionospheric network (CHAIN) during 2014—2022, this study proposes a PPP stochastic model suitable for geodetic GNSS receivers in high latitudes, referred to as the high latitude receiver tracking error (HL_RTES) model. The HL_RTES model uses S_4c index and rate of total electron content index (ROTI) to estimate the receiver tracking error variance, and the GPS positioning accuracy is improved by reweighting the observed values. The GPS observations of CHAIN station from February 1, 2023 to February 28, 2023 are used to conduct single-frequency mimics kinematic PPP experiment. Experimental results show that the performance of HL_RTES model and RTES model are comparable, and both can improve the PPP positioning accuracy under ionospheric scintillation; compared with EAS model, the monthly average RMS improvement rates of HL_RTES model in the horizontal, vertical and 3D directions are 33.3%, 42.8% and 38.3% respectively. In addition, using the data from the IGS stations INVK, KIRU, SCOR and URAL on February 15, 2023 to conduct experiment, it is found that the HL_RTES model can significantly mitigate the effects of high latitudes scintillation on PPP; compared with the EAS model, the improvement rates of HL_RTES model based PPP on the 3D RMS of the four stations are 57.2%, 32.9%, 43.8% and 31.4%, respectively.

Key words: ionospheric scintillation, PPP, stochastic model, geodetic receiver

CLC Number:

P228

Yingzong LIN, Xiaomin LUO, Junfeng DU. Fitting improvement of PPP receiver tracking error stochastic model for high latitudes[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 666-676.

Figures/Tables 10

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项目	策略
观测数据	L1频率的伪距和载波相位观测数据
采样间隔/s	30
截止高度角/(°)	10
精密卫星轨道	IGS精密产品星历
精密卫星钟差	IGS精密产品钟差
差分码偏差	CODE产品
相位缠绕	已经校正^[31]
天线相位中心偏移	igs14.atx
天线相位中心变化	igs14.atx
电离层延迟	作为参数进行估计
对流层延迟	使用Saastamoinen模型，其余部分作为随机游走进行估计
接收机坐标	作为白噪声进行估计和建模

测站	模型	RMS
测站	模型	东方向	北方向	高程方向	3D方向
ARCC	EAS	0.728	0.673	0.851	0.754
	RTES	0.255	0.344	0.509	0.384
	HL_RTES	0.253	0.453	0.767	0.535
CHUC	EAS	0.446	0.614	0.492	0.522
	RTES	0.144	0.379	0.236	0.271
	HL_RTES	0.304	0.387	0.260	0.321
DAWC	EAS	0.213	0.425	0.316	0.320
	RTES	0.307	0.398	0.286	0.334
	HL_RTES	0.146	0.316	0.245	0.246