High-precision Estimation of Roll and Pitch of Aircrafts

Abstract

Positioning plays a vital role in aircraft navigation. Pitch and roll estimation are two important aspects of aircraft positioning. In our research, we focused on a low-cost pitch/roll estimation platform, which includes a low-computation processor and a small-sized memory. With the platform, we compared the performance of two pitch/roll estimation methods: complementary filter based estimation and Kalman filter based estimation. Our experimental results indicate that, between the two approaches under investigation, Kalman filter-based estimation is much more precise. In addition, we found that R matrix, a critical variable of Kalman filter, has a serious impact on convergence time and stability of Kalman filter. When the entries of R matrix are set to low values, Kalman filter-based estimation leads to faster convergence time and poor stability. When they are set to high values, Kalman filter-based estimation is more stable, but it results in slow convergence.