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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 $\times \,\,70$ cm $\times \,\,50$ cm 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.

Magneto-Inertial Integrated Navigation System Design Incorporating Mapping and Localization Using Concurrent AC Magnetic Measurements