On the Impact of Optimized Stochastic Error Modeling on the Performance of EKF-based Baro-aided INS

Sensors’ stochastic error modeling is of utmost importance for sensitive navigation applications. Accelerometers, rate gyros and barometers are examples, whose measurements need to be calibrated in real-time for high-performance estimation. This work revisits the sensors aiding/integration/fusion problem via Extended Kalman Filter (EKF) to reliably estimate a given vehicle state. Historically, Allan Variance (AV) has been used to characterize the stochastic errors affecting the sensors and then tune the EKF, which involves a trade-off between optimal stochastic error characterization and optimal navigation performance. In this sense, a novel Genetic Algorithm (GA)based EKF-tuning methodology is investigated, and results from experimentally conducted tests show improvements, when compared to traditional techniques, for stabilizing the vertical channel error response. In this work, an EKF-based baroaided Inertial Navigation System (INS) under Global Navigation Satellite System (GNSS)-denied conditions is considered and the navigation performance is evaluated via altitude Root Mean Square Error (RMSE).