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This paper presents a real-time orientation estimation algorithm based on signals from a low-cost inertial measurement unit (IMU). The IMU consists of three MEMS accelerometers and three MEMS rate gyros. This approach is based on relationships between the quaternion representing the platform orientation, the measurement of gravity from the accelerometers, and the angular rate measurement from the gyros. Process and measurement models are developed, based on these relations, in order to implement them into an extended Kalman filter. The performance of each filter is evaluated in terms of the roll, pitch, and yaw angles. These are derived from the filter output since this orientation representation is more intuitive than the quaternion representation. Extensive testing of the filters with simulated and experimental data show that the filters perform very accurately in the roll and pitch angles, and even significantly corrects the yaw angle error drift.