Quaternionic Attitude Estimation for Robotic and Human Motion Tracking Using Sequential Monte Carlo Methods With von Mises-Fisher and Nonuniform Densities Simulations

In recent years, wireless positioning and tracking devices based on semiconductor micro electro-mechanical system (MEMS) sensors have successfully integrated into the consumer electronics market. Information from the sensors is processed by an attitude estimation program. Many of these algorithms were developed primarily for aeronautical applications. The parameters affecting the accuracy and stability of the system vary with the intended application. The performance of these algorithms occasionally destabilize during human motion tracking activities, which does not satisfy the reliability and high accuracy demand in biomedical application. A previous study accessed the feasibility of using semiconductor based inertial measurement units (IMUs) for human motion tracking. IMU hardware has been redesigned and an attitude estimation algorithm using sequential Monte Carlo (SMC) methods, or particle filter, for quaternions was developed. The method presented in this paper uses von Mises-Fisher and a nonuniform simulation to provide density estimation of the rotation group SO(3). Synthetic signal simulation, robotics applications, and human applications have been investigated.