Abstract
Recent research on inertial navigation systems has shown that better performance can be achieved by using a number of low-cost sensors and applying appropriate filters to data fusion. The purpose of this study is to use an array of low cost inertia sensors and data fusion using Extended Kalman Filter (EKF). Global Positioning System (GPS) is used to assist the inertial navigation system (INS) through the integration algorithm. Measurement may occur at each iteration of the EKF or at longer intervals. There is little space for sensors in flying devices, especially in quadcopters, especially GPS, so setting the appropriate distance between the sensors is essential. By adjusting the distance and the initial parameters of the sensors, the noise of the inertial sensors can be minimized. The effectiveness of the proposed design is shown using numerical simulations.
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