Dynamic Regulation of the Weights of Request Based on the Kalman Filter and an Artificial Neural Network

Magnetic and inertial measurement units (MIMU) are currently being explored as a promising tool for attitude tracking of a moving object, such as human body parts. The function of attitude calculation is realized by using attitude algorithms. The overall performance of these algorithms is seriously influenced by linear acceleration of the moving object. Therefore, there is a requirement to find solutions to this problem. In this paper, a new attitude algorithm for MIMU known as REQUEST is introduced. The algorithm is then revised in order to be suitable for MIMU. A new representation of linear acceleration of the moving object is then constructed. An artificial neural network (ANN) is used to establish the functional relationship between this representation and the weights assigned to the vector measurements in REQUEST, for the purpose of adaptively regulating the vector measurements according to the representation. In this way, the measurements of the gyroscope can be relied on more for attitude calculation when the carrier has a higher linear acceleration. A Kalman filter (KF) is also used prior to the establishment of the functional relationship in order to take full advantage of historical sensor information for accurate estimation of the representation. Our experiments have verified good static and dynamic performances of our KF+ANN-based REQUEST algorithm.