Multi-sensor Data Fusion Techniques for RPAS Navigation and Guidance
Francesco Cappello, Subramanian Ramasamy, Roberto Sabatini · RMIT Research Repository (RMIT University Library) · 2015
Integrated Navigation and Guidance Systems (NGS) based only on satellite andother low-costnavigationsensors(e.g., Micro-Electro-Mechanical System (MEMS) based inertial sensors) cannot guarantee theRequired NavigationPerformance(RNP)in all flight phasesof Remotely Piloted Aircraft Systems (RPAS).Inthis paper, a novel NGSforasmall-to-mediumsize RPASis presented, which is based on Global Navigation Satellite System (GNSS), Vision Based Navigation (VBN)and other low-cost avionics sensors.Additionally, Aircraft Dynamics Model (ADM)is used to compensatefor the MEMS based Inertial Measuring Unit (IMU)sensor shortcomings in high-dynamics attitude determination tasks. Two multi-sensor architectures are comparedthat are based on anExtended KalmanFilter (EKF)and anUnscented Kalman Filter (UKF) approach for data fusion. The ADM measurements are pre-filtered by an UKF to increasethe ADM attitude solution validity time.The EKF based VBN-IMU-GNSS-ADM(E-VIGA) system and the UKF basedsystem (U-VIGA) performances are evaluatedin a small RPAS integration scheme (i.e.,AEROSONDE RPAS platform) by exploring a representative cross-section of this RPAS operational flight envelope. Additionally,an error covariance analysis is performed on the Aircraft Dynamics Filter (ADF)using Monte Carlo simulation. Theposition and attitude accuracycomparisonshowsthat the E-VIGA and U-VIGA systems fulfilltherelevant RNPcriteria,including precision approach down to CAT-II.