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In this study, an estimation algorithm based on a two-stage Kalman filter (TSKF) was developed for wind speed and Unmanned Aerial Vehicle (UAV) motion parameters. In the first stage, the wind speed estimation algorithm is used with the help of the Global Positioning System (GPS) and dynamic pressure measurements. Extended Kalman Filter (EKF) is applied to the system. The state vector is composed of the wind speed components and the pitot scale factor. In the second stage, in order to estimate the state parameters of the UAV, GPS, and Inertial Measurement Unit (IMU) measurements are considered in a Linear Kalman filter. The second stage filter uses the first stage EKF estimates of the wind speed values. Between these two stages, a sensor fault detection algorithm is placed. The sensor fault detection algorithm is based on the first stage EKF innovation process. After detecting the fault on the sensor measurements, the state parameters of the UAV are estimated via robust Kalman filter (RKF) against sensor faults. The robust Kalman filter algorithm, which brings the fault tolerance feature to the filter, secures accurate estimation results in case of a faulty measurement without affecting the remaining good estimation characteristics. In simulations, noise increment and bias type of sensor faults are considered.

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SummaryAn adaptive integrated navigation system, consisting of barometric altimeter (Baro) and GPS, is presented. The integration is achieved by using an adaptive data fusion algorithm with the filter gain correction. Proposed adaptive data fusion algorithm utilizes the measurement noise scale factor in order to reduce the effects of the incorrect barometric altimeter measurements on the estimation procedure. By using the algorithm, incorrect baro measurements are corrected by the system without any impact on the good estimation behavior. Performance of the proposed Baro/GPS integrated system is examined by simulations for altitude estimation of an aircraft.