Sea surface height (SSH) serves as a fundamental geophysical parameter in oceanographic research. In 2023, China successfully launched the world’s first spaceborne interferometric GNSS-R (iGNSS-R) altimeter, which features dual-frequency multi-beam scanning, interferometric processing, and compatibility with three major satellite navigation systems: the BeiDou Navigation Satellite System (BDS), the Global Positioning System (GPS), and the Galileo Satellite Navigation System (GAL). This launch marked the first in-orbit validation of the iGNSS-R altimetry technology. This study provides a detailed overview of the iGNSS-R payload design and analyzes its dual-frequency delay mapping (DM) measurements. We developed a refined DM waveform-matching algorithm that precisely extracts the propagation delays between reflected and direct GNSS signals, enabling the retrieval of global sea surface height (SSH) through the interferometric altimetry model. For validation, we employed an inter-satellite crossover approach using Jason-3 and Sentinel-6 radar altimetry as references, achieving an unprecedented SSH accuracy of 17.2 cm at a 40 km resolution. This represents a breakthrough improvement over previous GNSS-R altimetry efforts. The successful demonstration of iGNSS-R technology opens up new possibilities for cost-effective, wide-swath sea level monitoring. It showcases the potential of GNSS-R technology to complement existing ocean observation systems and enhance our understanding of global sea surface dynamics.

To evaluate the prospects of multi-antenna GNSS buoy and mooring array in ocean altimetry satellite calibration, experiments are conducted in the ocean around Qianliyan islet in China’s Yellow Sea. The trials aim to validate the feasibility of establishing an ocean altimetry satellite calibration site while assessing the performance of relevant calibration equipment. Utilizing one multi-antenna GNSS buoy system and one mooring array operating for over 20 days, the experiment incorporates continuous GNSS observation data from Qianliyan islet’s permanent station. Results reveal that high-frequency sea surface height (SSH) signals exhibit periods approaching or below 10 s, with the designed low-pass filter effectively attenuating these high-frequency components. Significant differences emerge in the power spectra of filtered SSH measurements between instruments: high-frequency signals detected by the mooring array demonstrate greater spectral concentration and lower signal intensity than those recorded by the GNSS buoy. Through multi-day synchronized observations, the height datum for mooring array SSH measurements is obtained, revealing average standard deviation of 2.76 cm in filtered SSH differences between platforms—validating both the system design and data processing methodology. This experiment successfully demonstrates the performance of calibration equipment, preliminarily verifies the effectiveness of ground-based calibration data processing techniques, and further confirms the technical viability of establishing an ocean altimetry satellite calibration site around Qianliyan islet.

In view of the future development trend of ocean altimetry satellites, the design idea of Ka/C dual frequency combination system is proposed, that is, the mode of using an altimeter antenna to contain Ka and C frequencies (35.7 and 5.3 GHz, respectively) for combined ranging. First, the random error model of altimeter measuring the distance from the altimeter phase center to the sea surface is given, and the analysis shows that the ionospheric correction error is one of the important factors. Second, the calculation of typical ionospheric parameters shows that the influence of ionospheric higher order terms on altimeter ranging is below the mm level, and its influence can be ignored. The rigorous expression for the first-order and second-order term correction of ionosphere error are given, respectively, by using dual frequency and three frequencies. The computational experiment of Jason-2 and SARAL satellite show that the maximum magnitude of ionospheric error correction in Ku band can reach 11 cm, while the maximum magnitude of ionospheric error correction in Ka band can reach 2 cm. For high-precision applications, ionospheric corrections must be made for single Ka frequency. After Ka/C dual frequency combination is adopted, error correction can be directly conducted without global ionospheric map (GIM) model, and its accuracy will be further improved than GIM model. Under 1 Hz sampling conditions, the ionospheric correction accuracy of Ka/C combination can be better than 2.5 mm, basically eliminating the influence of the ionosphere, and the total ranging accuracy can reach 3.5 cm considering the propagation error.

Gravity-matching navigation is one of the important means for the covert navigation of underwater vehicles. The production and application of gravity reference maps as core key technologies have a very significant impact on the accuracy of underwater navigation. Firstly, this paper adopts the factor analysis method to obtain the comprehensive results of nine characteristic parameters such as the standard deviation and roughness of the gravity reference map by setting a range sliding window. Secondly, the TERCOM algorithm is introduced to conduct simulation verification calculations within the sliding window. After comparing and verifying with the comprehensive results of the factor analysis characteristic parameters, the limitations of statistical methods in the evaluation of the adaptability of gravity reference maps are analyzed. Thirdly, intelligent processing methods such as the learning vector quantization neural network algorithm and the extreme learning machine are proposed. The characteristic parameters of some sliding window gravity reference maps and the simulation verification results of the TERCOM algorithm are used as training samples to predict the adaptability evaluation effect of underwater gravity navigation for other sliding windows. The results show that the prediction results are generally in good agreement with the simulation verification results of the TERCOM algorithm. Compared with the learning vector quantization neural network algorithm, the extreme learning machine algorithm exhibits superior performance in terms of classification accuracy and computational efficiency.