Process and Measurement Noise Covarince Estimation Approach for Motion Capture Systems

This paper provides a comprehensive literature review for motion capture systems. We have summarized state-of-the-art literature of motion capturing systems with advantages and the drawbacks of different approaches. It can be seen that the Kalman filter (KF) is widely used for motion capture systems. Practical implementation of KF is very hard due to the difficulty of unknown noise covariances. So, the noise variation of KF is usually being assumed but this paper overcomes the aforementioned assumptions. Basically, this paper provides a method that estimates the noise covariances in state-space framework. In KF algorithm, the process and measurement noise covariance matrices can be estimated by the proposed method. The proposed method can be applied to the motion capture systems.