Su Tingli

Closed-Loop Estimation for Randomly Sampled Measurements in Target Tracking System

Many tracking applications need to deal with the randomly sampled measurements, for which the traditional recursive estimation method may fail. Moreover, getting the accurate dynamic model of the target becomes more difficult. Therefore, it is necessary to update the dynamic model with the real-time information of the tracking system. This paper provides a solution for the target tracking system with randomly sampling measurement. Here, the irregular sampling interval is transformed to a time-varying parameter by calculating the matrix exponential, and the dynamic parameter is estimated by the online estimated state with Yule-Walker method, which is called the closed-loop estimation. The convergence condition of the closed-loop estimation is proved. Simulations and experiments show that the closed-loop estimation method can obtain good estimation performance, even with very high irregular rate of sampling interval, and the developed model has a strong advantage for the long trajectory tracking comparing the other models.

Gesture detected by inertial sensor

In the wearable technology, inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movement of users fingers based on the inertial sensor to give the control signal. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the angle of the finger gesture was processed by the attitude matrix to get the attitude angle. The experimental results showed the detection of the finger movement is effective.