Estimation of attitudes from a low cost miniaturized13; inertial platform using Kalman filter based sensor fusion algorithm

Due to costs, size and mass, commercially available inertial navigation system are not suitable for small, autonomous flying vehicles like ALEX and other VAVs. In contrast using modem MEMS (or of similar class) sensors, the hardware costs, size and mass can be reduced substantially. However, sensors of low cost category suffer from inaccuracy and are influenced greatly by the temperature variation. In this report such inaccuracies and dependency on temperature variations have been studied, modeled and compensated in order to reach an13; adequate quality of the measurementsfor the specific application. This has been done applying a Kalman filter based sensor fusion algorithm that combines sensor models, error parameters and estimation scheme. The attitude estimation from low cost sensors is first realized in ATLAB/SIMULINK platform and then implemented on hardware (MIP2) by programming the micro controller and validated. The accuracy of the estimated roll and pitch attitudes are well within the stipulated accuracy level of :tSo for the ALEX. However the estimation of heading angle which is mainly derived from the magnetometerreadings seems to be influenced greatly by the variation in local magnetic field