Miao Beibei

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.