Harmonization of a multi-sensor navigation system

Abstract

Abstract-We address the problem of harmonization of two subsystems used separately to measure a velocity in a body reference frame and an attitude with respect to an inertial frame. This general problem is of particular importance when the two subsystems are jointly used to obtain an estimate of the inertial velocity, and, then, the position through an integration procedure. Typical possible applications encompass pedestrian navigation, and, more generally, indoor navigation. The paper demonstrates how harmonization can be achieved thanks to an analysis of propagation of projection errors. In particular, trajectories known to be closed can be used to determine one dominant harmonization factor which is the pitch relative orientation angle between the two subsystems. Computations yielding the harmonization procedure are exposed in this article, and experimental data serve to illustrate the application of the proposed method.

MAINS: A Magnetic Field Aided Inertial Navigation System for Indoor Positioning

“…The seminal work [8] derived an equation that relates the body velocity and the gradient of the magnetic field, which can be calculated from the measurements from a set of spatially distributed magnetometers. Later, in [14], [15], the authors proposed an observer to estimate body velocity, proved its convergence, and showcased its usability for indoor localization. In subsequent works [16], [17], the authors incorporated inertial sensor biases and magnetic disturbance in the model and designed a filter based on the error-state Kalman filter (ESKF).…”

Further results on the observability in magneto-inertial navigation

“…The only unknown term is the linear velocity, which can thus be estimated based on the knowledge of the other terms and the governing system dynamics. In order to estimate the linear velocity, state observers are employed and several versions, relying on various modeling assumptions for the unknown linear velocity, have been proposed in [4] and [5]. Their analysis are usually performed by a point-wise analysis of observability [5] or a Lyapunov analysis [3], calling for careful application of the invariance principle in linear timevarying cases.…”