Jin Xuebo

Target Tracking of a Linear Time Invariant System Under Irregular Sampling

Due to event-triggered sampling in a system, or maybe with the aim of reducing data storage, tracking many applications will encounter irregular sampling time. By calculating the matrix exponential using an inverse Laplace transform, this paper transforms the irregular sampling tracking problem to the problem of tracking with time-varying parameters of a system. Using the common Kalman filter, the developed method is used to track a target for the simulated trajectory and video tracking. The results of simulation experiments have shown that it can obtain good estimation performance even at a very high irregular rate of measurement sampling time.

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.

Compression processing estimation method for time series big data

The rapid development of computer science has caused an explosion of mining interest in the time series big data domain. Thus the data processing architecture has been proposed to meet the demand for optimizing the performance of systems. This paper presents an implementation of data processing methods for uncertain time series big data with noise. The Kalman filter, an estimation technique to extract high dimension characteristics of states in the target tracking field, is adaptive and can guarantee tracking target states with certain measurement range. Thanks to the Kalman filter, we can compress a datasets by irregularly sampling observation data, which is called the compression processing estimation method (CPEM). The simulation results and its comparisons to the mean value method (MVM) show that we can quickly, accurately extract important information of time series and get a good compression result.

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.