By aiding GPS receiver tracking loops with INS estimates of signal dynamics, GPS/INS ultra-tight coupling can improve the navigation performance in challenging environments. Traditionally the INS data are injected into the loops once every loop update interval, which limits the levels of dynamics accommodated. This paper presents a sample-wise aiding method, which interpolates the aiding Doppler into each digital sample of the local signal to further eliminate the dynamic errors. The relationship between the tracking error and the aiding rate is derived analytically. Moreover, the effects of sample-wise aiding using linear and spline interpolations are simulated and compared with traditional aiding under different INS data update rates. Finally, extensive tests based on a digital IF (intermediate frequency) signal simulator and a software receiver validate the theoretical equations and demonstrate that the dynamic stress error can be significantly reduced by sample-wise aiding.

Ocean surface wind remote sensing using reflectometry measurements from S‐band (2.3 GHz) digital communication satellite transmissions is demonstrated in tropical cyclones. Twelve days of airborne data were collected during the 2014 Atlantic hurricane season. An empirical model function, relating the sensed ocean surface mean square slope to wind speed, was first developed by fitting mean square slope estimates to comparison wind speed data from the Stepped Frequency Microwave Radiometer on the same aircraft and wind direction data from the Hurricane Weather Research and Forecasting model. This model function was then applied for wind speed retrievals using an independent set of reflectometry measurements and validated against four data sources: Stepped Frequency Microwave Radiometer, the Hurricane Weather Research and Forecasting model, dropsondes, and flight‐level winds. Good agreement between these retrievals and comparison data was found, with a root‐mean square error between 4.9 and 6.6 m/s and a bias between −1.4 and −0.2 m/s for wind speeds up to 50 m/s.

Reliability and validity of model prediction play a decisive role in water resource simulation and prediction. Among many prediction models, the combined model (CM) is widely used because it can combine the prediction results of multiple single models and make full use of the information provided by various methods. CM is an effective method to improve the predictive veracity but the weight of single model estimation is the key to the CM. Previous studies take errors as the objective function to calculate the weight, and the uncertainty of the weight of the individual model cannot be considered comprehensively. In order to consider the uncertainty of the weight and to improve universal applicability of the CM, in this paper, the authors intend the Markov chain Monte Carlo based on Adaptive Metropolis algorithm (AM-MCMC) to solve the weight of a single model in the CM, and obtain the probability distribution of the weight and the joint probability density of all the weight. Finally, the optimal weight combination is obtained. In order to test the validity of the established model, the author put it into the prediction of monthly groundwater level. The two single models in the CM are time series analysis model (TSAM) and grey model (GM(1,1)) respectively. The case study showed that the uncertainty characteristic of the weight in the CM can be obtained by AM-MCMC. According to the study results, CM has obtained a least average root mean square error (RMSE) of 0.85, a mean absolute percentage error (MAPE) of 8.61, and a coefficient of determination (R²) value of 0.97 for the studied forecast period.