A regional GNSS-VTEC model over Nigeria using neural networks： A novel approach

A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere＇s （IRI＇s） critical plasma frequency （foF2） parameter as an additional neuron for the network＇s input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time （DST） and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network （NIGNET） were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI＇s foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

Improving the underwater navigation performance of an IMU with acoustic long baseline calibration

Underwater acoustic Long-Baseline System(LBL)is an important technique for submarine positioning and navigation.However,the high cost of the seafloor equipment and complex construction of a seafloor network restrict the distribution of the LBL within a small area,making an underwater vehicle difficult for long-distance and high-precision acoustic-based or inertial-based navigation.We therefore propose an acoustic LBL-based Inertial Measurement Unit(IMU)calibration algorithm.When the underwater vehicle can receive the acoustic signal from a seafloor beacon,the IMU is precisely calibrated to reduce the cumulative error of Strapdown Inertial Navigation System(SINS).In this way,the IMU is expected to maintain a certain degree of accuracy by relying solely on SINS when the vehicle reaches out the range of the LBL network and cannot receive the acoustic signal.We present the acoustic LBL-based IMU online calibration model and analyze the factors that affect the accuracy of IMU calibration.The results fulfill the expectation that the gyroscope bias and accelerometer bias are the main error sources that affect the divergence of SINS position errors,and the track line of the underwater vehicle directly affects the accuracy of the calibration results.In addition,we deduce that an optimal calibration trajectory needs to consider the effects of the three-dimensional observability and position dilution of precision.In the experiment,we compare the effects of seven calibration trajectories:straight and diamond-shaped with and without the change of depth,and three sets of curves with the change of depth:circular,S-shaped,and figure-eight.Among them,we find that the figure-eight is the optimal trajectory for acoustic LBL-based IMU online calibration.We take the maintenance period during which the accumulated SINS Three Dimensional(3D)position errors are below 1 km to evaluate the calibration performance.The filed experimental results show that for the Micro-electromechanical Systems-grade IMU sensor,the maintenance period for the IMU calibrated with the proposed algorithm can be increased by 121%and 38.9%compared to the IMU without calibration and with the laboratory default parameter calibration,indicating the effectiveness of the proposed calibration algorithm.