An improved snow depth retrieval method with adaptive noise reduction for GPS/GLONASS/Galileo/BDS multi-frequency signals

Abstract

The Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique is an effective method to monitor snow depth. The detrended signal-to-noise ratio (dSNR) series is analyzed by Lomb–Scargle periodogram (LSP) to extract the characteristic frequency, which can be converted to the snow depth. However, the dSNR data are greatly affected by noise in the observation environment, which leads to the abnormal characteristic frequency and low accuracy of snow depth retrieval. In order to reduce the influence of noise and to ensure the correct extraction of the characteristic frequency, we present an improved adaptive retrieval method for multi-constellation retrieval scenario. Firstly, the dSNR sequences are decomposed adaptively into several Singular Spectrum Components (SSCs) with different frequency scales by Singular Spectrum Decomposition (SSD). Then, the corresponding SSCs are selected, according to the empirical scope of snow depth, to reconstruct the “pure” dSNR series. Finally, the reconstructed signals are analyzed by LSP to derive the characteristic frequency, in order to obtain the snow depth. The multi-GNSS observations of site SG27 (Alaska, USA) and site P351 from Plate Boundary Observation network in a representative period from winter 2019 to spring 2020 were used to validate the proposed method. The snow depths were estimated from individual signals, individual constellations and multi-GNSS combination using both the traditional and the improved methods. The experimental results show that compared with the traditional method, the snow depth trend of the improved method is more consistent with the measured snow depth trend, especially in the early stage of snowfall. Furthermore, the proposed method shows a universal applicability to various signals of GPS, GLONASS, Galileo and BDS and the retrieval accuracy of all signals are improved in different degrees. When using multi-GNSS combination signals, the mean bias and RMSE of multi-GNSS snow depth retrieval at site SG27 are improved from 4.6 and 6.2 cm to 4.2 and 5.4 cm, respectively. The mean bias and RMSE at site P351 are improved from 10.5 and 12.4 cm to 9.5 and 11.5 cm, respectively.

GNSS-R snow depth retrieval algorithm based on PSO-LSTM

“…In the field of remote sensing, GNSS-R technology has received increasing attention, and many scientists are continuously researching it. GNSS-R technology has been widely used to monitor sea level height [4][5][6], snow depth [7][8][9], soil moisture [10][11][12], and sea ice detection [13,14], etc. In the field of snow depth retrieval technology, Larson et al [15] used global positioning system (GPS) receivers and traditional methods to monitor heavy snowfall, respectively, and the experimental results showed that the snow depth could be effectively monitored using GPS receivers, and the monitoring results were very similar to the classical methods.…”

Influence of sweep interference on satellite navigation time-domain anti-jamming

“…The Global Navigation Satellite System (GNSS) is satellite-based all-weather navigation, positioning, and timing system [1][2][3]. At present, the leading satellite navigation systems used around the world are the US GPS (Global Positioning System), the Chinese Beidou-3 global satellite navigation system, the Russian GLONASS (Global Navigation Satellite System), and the European Galileo system [4][5][6][7]. With the deterioration of the electromagnetic environment and the escalation of interference complexity, including concepts such as electronic warfare and navigation warfare, which have received a great deal of attention in modern combat systems, anti-jamming has become an essential function of satellite navigation receivers [8][9][10][11].…”

Enhancing BDS-3 PPP-AR with observable-specific signal biases

“…BDS-3 comprises satellites in three distinct orbits, namely the medium The BDS-3 multi-frequency signals, coupled with the three kinds of satellites in MEO, IGSO and GEO orbits, are increasing the research dimension in the GNSS community. In both BDS-only and multi-GNSS constellations, a considerable volume of research has followed in different fields such as multipath [2,3], signal-to-noise ratio (SNR) [4], GNSS interferometric reflectometry [5,6], quality of BDS products [7,8], precise time transfer [9,10], and ambiguity resolution (AR) [11]. While multi-frequency and multi-constellation GNSS may be applied in such broad areas, the increase in signals itself complicates the treatment of biases in the formulated observables.…”