Abstract
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, performance analysis for velocity measurements with lever-arm aided INS is made for different maneuvers. Two error-states models are used in the analysis. Simulation results show the sensitivity of the error-states to lever arm and vehicle maneuver.
Highlights
Navigating air, sea and land vehicles using multi-sensor navigation systems has been subject to increasing interest in the literature
When performing this multi-sensor integration, one must take into account the effect of leverarm, which is usually referred to the relative position between the sensors mounted on the vehicle
The STD values are decreasing slow and not as low as the 12 error state model due to the uncertainty in the lever-arm elements. This weaken the solution of the 15 error state model, especially in rotation maneuvers when the lever-arm is expressed in the measurements model
Summary
Navigating air, sea and land vehicles using multi-sensor navigation systems has been subject to increasing interest in the literature. From the overgrowing need for high precision and reliable navigation systems and through the diverse potential for independence and automotive platforms, navigation based on multi-sensor fusion will allow to utilize the advantages of each individual sensor to optimize the process performance [1,2,3,4]. When performing this multi-sensor integration, one must take into account the effect of leverarm, which is usually referred to the relative position between the sensors mounted on the vehicle. Proceedings 2018, 2, 138 not included in the error state, and to the 15 errors state model, where the lever-arm error is included with three more error-state
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