The carrier phase multipath effect is one of the most significant error sources in the precise positioning of BeiDou Navigation Satellite System (BDS). We analyzed the characteristics of BDS multipath, and found the multipath errors of geostationary earth orbit (GEO) satellite signals are systematic, whereas those of inclined geosynchronous orbit (IGSO) or medium earth orbit (MEO) satellites are both systematic and random. The modified multipath mitigation methods, including sidereal filtering algorithm and multipath hemispherical map (MHM) model, were used to improve BDS dynamic deformation monitoring. The results indicate that the sidereal filtering methods can reduce the root mean square (RMS) of positioning errors in the east, north and vertical coordinate directions by 15%, 37%, 25% and 18%, 51%, 27% in the coordinate and observation domains, respectively. By contrast, the MHM method can reduce the RMS by 22%, 52% and 27% on average. In addition, the BDS multipath errors in static baseline solutions are a few centimeters in multipath-rich environments, which is different from that of Global Positioning System (GPS) multipath. Therefore, we add a parameter representing the GEO multipath error in observation equation to the adjustment model to improve the precision of BDS static baseline solutions. And the results show that the modified model can achieve an average precision improvement of 82%, 54% and 68% in the east, north and up coordinate directions, respectively.

While the efficiency of incorporating phenology features into vegetation type classification, in general, and coastal wetland vegetation classification, in particular, has been verified, it is difficult to acquire high-spatial-resolution (HSR) images taken at appropriate times for vegetation identification using phenology features because of the coastal climate and the HSR satellite imaging cycle. To strengthen phenology feature differences, in this study, we constructed vegetation phenology metrics according to vegetation NDVI time series curves fitted by samples collected from the Linhong Estuary Wetland and Liezi Estuary Wetland based on Gao Fen (GF) series satellite images taken between 2018 and 2022. Next, we calculated the phenology metrics using GF series satellite imagery taken over the most recent complete phenology cycle: 21 October 2020, 9 January 2021, 19 February 2021, and 8 May 2021. Five vegetation type classifications in the Linhong Estuary Wetland were carried out using single images of 21 October 2020 and 8 May 2021, along with their combination and the further addition of phenology metrics. From our comparison and analysis, the following findings emerged: Combining the images taken in 21 October 2020 and 8 May 2021 provided better vegetation classification accuracy than any single image, and the overall accuracy was, respectively, increased from 47% and 48% to 67%, while the corresponding kappa was increased from 33% and 34% to 58%; however, adding phenology metrics further improved the accuracy by decreasing the effect of some confusion among different vegetation types, and the overall accuracy and kappa were further improved to 75% and 69%, respectively. Though some problems remain to be further dealt with, this exploration offers helpful insights into coastal wetland vegetation classification using phenology based on HSR imagery.

Orthogonal frequency division multiplexing (OFDM) is widely known as the promising broadband wireless communication technology due to the high spectral efficiency and robustness to the multi-path interference. However, the inter-carrier interference (ICI) caused by Doppler frequency offset is an important issue for OFDM in high mobility. In this paper, the method to mitigate ICI for OFDM system is investigated, and a novel ICI mitigation method using planar extended Kalman filter (PEKF) is proposed to reduce the ICI effects. Simulation results demonstrate that the proposed ICI mitigation method in this letter outperforms traditional ICI mitigation methods.

With the help of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) technology, high spatial and temporal resolution surface deformation results can be generated, which can help to better understand the mechanism of surface deformation. The spatio-temporal Kalman-based InSAR and GNSS fusion method fully considers the spatio-temporal correlation of deformation, and characterizes the potential spatio-temporal process of deformation through spatial modeling and Kalman filter or smooth. However, when focusing on deformation with typical subsidence spatial characteristics, existing studies did not fully consider the spatial distribution of deformations, resulting in the loss of spatial detail information. This article proposes an adaptive spatial modeling optimization method that takes into account the spatial distribution of deformation, which can capture the optimal spatial basis layout scheme under a limited number of spatial bases, establish a more accurate spatial model, and improve the accuracy of deformation fusion. The effectiveness and reliability of this method are verified through simulation experiments and the deformation monitoring results in Datong City, China. The results of the two experiments show that the proposed method can improve the accuracy of InSAR interpolation results by 27.7% and 11.5% on average, respectively