A navigation algorithm is proposed to increase the inertial navigation performance of a ground vehicle using magnetic measurements and dynamic constraints. The navigation solutions are estimated based on inertial measurements such as acceleration and angular velocity measurements. To improve the inertial navigation performance, a three-axis magnetometer is used to provide the heading angle, and nonholonomic constraints (NHCs) are introduced to increase the correlation between the velocity and the attitude equation. The NHCs provide a velocity feedback to the attitude, which makes the navigation solution more robust. Additionally, an acceleration-based roll and pitch estimation is applied to decrease the drift when the acceleration is within certain boundaries. The magnetometer and NHCs are combined with an extended Kalman filter. An experimental test was conducted to verify the proposed method, and a comprehensive analysis of the performance in terms of the position, velocity, and attitude showed that the navigation performance could be improved by using the magnetometer and NHCs. Moreover, the proposed method could improve the estimation performance for the position, velocity, and attitude without any additional hardware except an inertial sensor and magnetometer. Therefore, this method would be effective for ground vehicles, indoor navigation, mobile robots, vehicle navigation in urban canyons, or navigation in any global navigation satellite system-denied environment.

Increasing the number of satellites in a global navigation satellite system (GNSS) improves the positioning accuracy and increases availability. However, it reduces the positioning accuracy improvement rate and increases the calculation loads, which can cause battery usage problems in mobile devices using a GNSS. An appropriate satellite selection method is required. One current method entails the use of ideal satellite placement with respect to the minimum geometric dilution of precision (GDOP). In this study, the described ideal satellite placement with the minimum GDOP were divided in terms of the horizontal dilution of precision (HDOP) and vertical dilution of precision (VDOP). HDOP and VDOP were mathematically derived and analyzed. The derived formula was verified using simulations. The analysis was performed with actual dual GNSS satellite data. The satellites adjacent to the ideal placement were selected and the DOP was calculated. Simply selecting satellites closest to the ideal placement afforded large values for HDOP and VDOP. This issue was addressed using a satellite changing algorithm considering the dual GNSS, resulting in reduced values of the HDOP and VDOP.

A new artificially generated magnetic-field-based alternative navigation technique is investigated for achieving seamless navigation in non-line-of-sight (NLOS) environments where satellite navigation is unavailable, and where vision, lidar, and other sensors that make use of signals of opportunity cannot provide reliable estimation. Unlike the previously reported multilateration methods, which make use of the magnetic receiver coil’s voltage model, the proposed method first extracts three-dimensional field vectors from concurrent ac magnetometer measurements, then establishes the spatial representation model of magnetic vectors for localization purpose. It does so by separating each frequency component and developing a geometric matching procedure using the interpolated data from the grid map in order to achieve a pose estimation. This study further presents an integrated navigation algorithm that associates an inertial measurement with the estimated pose information to secure a continuous navigation solution at a high output rate, where the extended Kalman filter (KF) formulation is typically employed. Finally, in order to validate the estimation performance of the proposed method, a small-scale trajectory experiment in 70cm × 70cm × 50cm test platform was performed, by which the integrated navigation was verified as possessing an average position accuracy in the sub-cm range as well as a heading angle error in the sub-degree range.