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With the development of Global Navigation Satellite Systems (GNSS), the multipath effect that was once regarded as one error source has been proven can be used to monitor reflecting surface parameters such as water level, snow depth, vegetation water content, and soil moisture using a technology named GNSS-interferometry reflectomety (GNSS-IR). In order to expand monitoring objects and applications of GNSS-IR, this paper proposed the theory and method to retrieve wind speed using ground-based GNSS-IR. The theory includes the signal-to-noise ratio (SNR) multipath oscillation theory, the scattering model theory, and wave spectrum theory. This paper simulated the mathematical model between the SNR cutoff elevation angle and the wind speed, and used the wavelet analysis to extract SNR cutoff elevation angle and a unified method to unify reference of cutoff elevation angle. Data of HKQT station, located in Hong Kong, during the period of "Mangkhut" and "Hato" were used. The results showed that the variations of GPS L5 SNR cutoff elevation angles had a good corresponding relationship with the measured wind speeds, and the correlation coefficient reached 70%~85%. Also, the variations of cutoff elevation angles can describe the change of wind speed from low wind speed to high wind speed and there was a specific mathematical relationship between them. Finally, this paper discussed two difficulties to overcome in the GNSS-IR wind speed retrieval technology.

The ionospheric scintillation, as one of the astronomical disasters occurring frequently in Arctic regions, poses great challenges to GNSS positioning navigation and timing (PNT) services. This calls for an urgent need in studying and effectively monitoring the scintillation to overcome its adverse impact. With the capability of high frequency sampling, ionospheric scintillation monitoring receivers (ISMR) are usually required to monitor the ionospheric scintillation, but the distribution of ISMR restricts the comprehensive monitoring in larger areas (such as the Arctic region). Therefore, based on GNSS observations with 1 Hz sampling, this paper studies the relevant empirical parameters and methods of extracting the ionospheric scintillation signal from the carrier phase observations by using geodetic detrending, precise point positioning and wavelet transform techniques, to construct a new phase scintillation index, which can be used to monitor the ionospheric scintillation. Its effectiveness and accuracy are verified by 188-day observations from 11 stations provided by the Canadian High Arctic Ionospheric Network (CHAIN). The results show that, compared with the commonly used ROTI index, both the scintillation index proposed in this paper and ROTI can effectively detect the occurrence of ionospheric scintillation, but the scintillation index proposed in this paper has a better correlation with the phase scintillation index given by ISMR, especially during periods with strong ionospheric scintillation, indicating that the proposed scintillation index has better ionospheric scintillation monitoring capability.

Seafloor classification is of great significance for the development and utilization of marine resources and marine scientific research. At present, multibeam detection is one of the effective methods to achieve large-scale seafloor classification. Seafloor classification is usually based on the angular response (AR) features and backscatter image features extracted by using multibeam backscatter. Because the feature source is relatively single and classifier structure is simple, the classification accuracy is often not high. This paper proposes a seafloor classification method based on convolutional neural networks (CNN). In addition to backscatter features, bathymetry features are also used to classify. The feature vectors are converted into waveform maps, and then input to the convolutional neural network for training and classification. The experiment compares different feature combination models and four conventional classifiers: BP network, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). The overall classification accuracy of the experiment reaches 94.86%, the kappa coefficient up to 0.93, and it takes 1 min 25 s. The accuracy has obvious advantages and the efficiency is relatively high. This method can effectively obtain the seafloor information in two different data types, give full play to the characteristics of convolutional neural network weight sharing, high efficiency, and achieve high-resolution seafloor classification. This paper provides a reference for the seafloor classification based on multibeam.

The lack of an effective accuracy assessment method of geoid is a key problem in the modernization of height datum and its application. In this paper, according to the error characteristics of GNSS-leveling height anomaly and gravity field in frequency domain, the technical requirements for the fusion of GNSS-leveling and gravity height anomaly are studied. Furthermore, a method of error expression and accuracy evaluation of geoid is proposed. Through the example test and analysis, the main conclusions are as follows: ①The accuracy of the fused height anomaly should be expressed by the error curve of height anomaly difference varying with the distance. ②For accuracy assessment of quasigeoid, two error indicators and two error curves are recommended, namely, the error of gravity height anomaly difference, the internal error of the fused height anomaly, the error curve of the fused height anomaly difference and the error curve of GNSS-levelling height anomaly difference. ③When the distance between two terrestrial points is close to or less than the average distance between GNSS leveling points, GNSS-levelling height anomaly plays a major role in the contribution of the fused height anomaly. ④The accuracy of the fused height anomaly in large scale is mainly controlled by gravity height anomaly.

With the increasing number of high orbit spacecraft, the use of high orbit GNSS technology to a certain extent avoids the limitations of traditional ground-based measurement and orbit determination, reduces the operation burden and maintenance cost of ground-based measurement and communication, and improves the autonomous navigation ability and orbit measurement accuracy of the satellite platform. Therefore, GNSS technology has significant research significance and application value for high earth orbit spacecraft. In this paper, the technology development of GNSS used in high orbit spacecraft is reviewed. Compared with low orbit spacecraft, the characteristics of GNSS in high orbit space are analyzed. From the three aspects of high orbit GNSS availability, on orbit technology verification and high sensitive receiver technology, the characteristics and development of high orbit GNSS autonomous navigation technology are summarized. The new tasks of high orbit post precise orbit determination and formation satellite relative navigation are analyzed. From the perspective of the future development of high orbit spacecraft, the future research prospect of high orbit GNSS technology is given.