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<TITLE>Abstract AAS 97-683</TITLE>
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<h2>AAS 97-683</h2>
<h2> AN ADAPTIVE TECHNIQUE FOR ESTIMATING ATTITUDE FROM EARTH MAGNETIC FIELD DATA </h2>
<h4> F. Curti - University of Rome La Sapienza, Rome, Italy </h4>
<h2> Abstract </h2>
The classical estimation technique for nonlinear systems is to linearize the dynamics and the measurement functions around the state estimate and apply the Extended Kalman Filter (EKF) theory. However, the applicability of the EKF is affected by the availability of accurate dynamical and measurement models. The main idea proposed in this paper is to employ an adaptive scheme for the estimator so as to eliminate the need to know the exact dynamical and measurement models. We obtain an adaptive estimator, named Nonlinear Adaptive State Estimator (NASE), which uses the sensor outputs building the estimator gain directly. To study the algorithm stability a suitable Lyapunov function is selected, which depends on the output error and the NASE gain. Consequently, the NASE is functioning as an optimal filter, not in the Kalman sense, but minimizing the selected Lyapunov function. The algorithm has been applied for estimating attitude, by using the earth magnetic field data, of the Italian