Temperature-Dependent Baro-Aided INS for GNSS-Denied Intelligent Vehicle Applications

Sensor fusion is of paramount importance for Intelligent Autonomous Vehicles (IAVs) nowadays. Global Navigation Satellite Systems (GNSSs), in particular, are present in most strategic applications, bounding the drift of Inertial Navigation Systems (INSs). When the former are not available, either due to signal blockage or deliberate jamming/spoofing, barometers are sensors that can maintain INS vertical channel accuracy in the long-term. In most baro-aided INS integrations currently seen in the literature, however, a standard constant-temperature gradient is assumed for the barometric atmospheric pressure model, which might provide less-than-optimal performance in environments subject to extreme temperature variations (such as deserts). As main contribution of this paper, we propose an improved Tightly-Coupled (TC) Extended Kalman Filter (EKF)-based baro-INS integration that employs actual Outside Air Temperature (OAT) measurements from an external temperature probe. Results from experimental tests conducted in a desert area show that the proposed approach outperforms the traditional ones, particularly when the GNSS is unavailable for long periods of time, and the IAV is subject to large altitude excursions.