The navigation subsystem in most platforms is based on an inertial navigation system (INS). Regardless of the INS grade, its navigation solution drifts in time. To avoid such a drift, the INS is fused with external sensor measurements such as a global navigation satellite system (GNSS). Recent publications showed that the lever-arm, defined as the relative position between the INS and aiding sensor, has a strong influence on navigation accuracy. Most research in this field is focused on INS/GNSS fusion with GNSS position or velocity updates while considering various maneuvers types. In this paper, we propose to employ virtual lever-arm (VLA) measurements to improve the accuracy and time to convergence of the observable INS error-states. In particular, we show that VLA measurements improve performance even in stationary conditions. In situations when maneuvering helps to improve state observability, VLA measurements manage to gain additional improvement in accuracy. These results are supported by simulation and field experiments with a vehicle mounted with a GNSS and an INS.

In most autonomous vehicles the navigation subsystem is based on Inertial Navigation System (INS). Regardless of the INS grade, its navigation solution drifts in time. To avoid such a drift, the INS is fused with external sensor measurements. Recent publications show that the lever-arm, the relative position between the INS and aiding sensor, has influence on the navigation performance.

Most published research in this field is focused on INS/GNSS fusion with GNSS position updates only where performance and analytical observability analysis were made to examine the consequence of vehicle maneuvers on the estimation of the lever-arm states.

Yet, besides position updates, a variety of sensors measuring the vehicle velocity vector are available including GNSS and a Doppler velocity log. As in position measurements, when performing INS/velocity measurements fusion, the lever-arm must be taken account for. In this paper, an analytical observability and performance analysis for velocity measurements with lever-arm aided INS is derived for stationary conditions. The observable and unobservable subspaces are derived for two error-stets models: 1) a 12 error-state model (the position and lever-arm error-states are not included) yet the lever-arm is present in the measurement equation and 2) a 15 error-state model including the lever-arm error-states. The analytical closed form expressions are verified by numerical simulation.