Dipolar elementary current systems for ionospheric current reconstruction at low and middle latitudes

The technique of spherical elementary current systems (SECS) is a powerful way to determine ionospheric and field-aligned currents (FAC) from magnetic field measurements made by low-Earth-orbiting satellites, possibly in combination with magnetometer arrays on the ground. The SECS method consists of two sets of basis functions for the ionospheric currents: divergence-free (DF) and curl-free (CF) components, which produce poloidal and toroidal magnetic fields, respectively. The original CF SECS are only applicable at high latitudes, as they build on the assumption that the FAC flow radially into or out of the ionosphere. The FAC at low and middle latitudes are far from radial, which renders the method inapplicable at these latitudes. In this study, we modify the original CF SECS by including FAC that flow along dipolar field lines. This allows the method to be applied at all latitudes. We name this method dipolar elementary current systems (DECS). Application of the DECS to synthetic data, as well as Swarm satellite measurements are carried out, demonstrating the good performance of this method, and its applicability to studies of ionospheric current systems at low and middle latitudes.

Research on the calibrated method for MEMS magnetometer arrays

The MEMS magnetometer determines the orientation for the MEMS inertial system. Because of the large noise of the MEMS magnetometer and the interference of soft and hard iron outside, the measurement error of the MEMS magnetometer is large. To reduce the effects of the random noises, the MEMS magnetometer arrays are designed in this paper. In our design, thirty-two MEMS magnetometers are welding on a printed circuit board (PCB), which area is 5×5 cm2. The forty general-purpose input-output (GPIO) ports, which are thirty-two data ports and eight clock ports, are used to collect the data of MEMS magnetometers. Then, averaging the thirty-two measurements of the MEMS magnetometers, the random noises of the measurements of the MEMS magnetometers can be reduced. Based on the averaging operation for the collected sensors’ data, a unified measurement model for the MEMS magnetometer arrays is constructed. Using the unified measurement model, an adaptive Kalman filter is developed to estimate the unknown parameters. To validate the performance of the MEMS magnetometer arrays, the simulation and experimental tests are designed. The test results show that, comparing with the single MEMS magnetometer, the random noises of the MEMS magnetometer arrays are reduced effectively.

Research on the calibrated method for MEMS magnetometer arrays

The MEMS magnetometer determines the orientation for the MEMS inertial system. Because of the large noise of the MEMS magnetometer and the interference of soft and hard iron outside, the measurement error of the MEMS magnetometer is large. To reduce the effects of the random noises, the MEMS magnetometer arrays are designed in this paper. In our design, thirty-two MEMS magnetometers are welding on a printed circuit board (PCB), which area is 5×5 cm2. The forty general-purpose input-output (GPIO) ports, which are thirty-two data ports and eight clock ports, are used to collect the data of MEMS magnetometers. Then, averaging the thirty-two measurements of the MEMS magnetometers, the random noises of the measurements of the MEMS magnetometers can be reduced. Based on the averaging operation for the collected sensors’ data, a unified measurement model for the MEMS magnetometer arrays is constructed. Using the unified measurement model, an adaptive Kalman filter is developed to estimate the unknown parameters. To validate the performance of the MEMS magnetometer arrays, the simulation and experimental tests are designed. The test results show that, comparing with the single MEMS magnetometer, the random noises of the MEMS magnetometer arrays are reduced effectively.

Research on the calibrated method for MEMS magnetometer arrays

The MEMS magnetometer determines the orientation for the MEMS inertial system. Because of the large noise of the MEMS magnetometer and the interference of soft and hard iron outside, the measurement error of the MEMS magnetometer is large. To reduce the effects of the random noises, the MEMS magnetometer arrays are designed in this paper. In our design, thirty-two MEMS magnetometers are welding on a printed circuit board (PCB), which area is 5×5 cm2. The forty general-purpose input-output (GPIO) ports, which are thirty-two data ports and eight clock ports, are used to collect the data of MEMS magnetometers. Then, averaging the thirty-two measurements of the MEMS magnetometers, the random noises of the measurements of the MEMS magnetometers can be reduced. Based on the averaging operation for the collected sensors’ data, a unified measurement model for the MEMS magnetometer arrays is constructed. Using the unified measurement model, an adaptive Kalman filter is developed to estimate the unknown parameters. To validate the performance of the MEMS magnetometer arrays, the simulation and experimental tests are designed. The test results show that, comparing with the single MEMS magnetometer, the random noises of the MEMS magnetometer arrays are reduced effectively.