The composition of working principle and calibration status of LNG (Liquefied Natural Gas) dispenser in China are introduced. According to the defect of weighing method in the calibration of LNG dispenser, LNG dispenser verification device has been researched. The verification device bases on the master meter method to verify LNG dispenser in the field. The experimental results of the device indicate it has steady performance, high accuracy level and flexible construction, and it reaches the international advanced level. Then LNG dispenser verification device will promote the development of LNG dispenser industry in China and to improve the technical level of LNG dispenser manufacture.
Proc. SPIE. 7283, 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment
KEYWORDS: Detection and tracking algorithms, Particles, Complex systems, Estimation theory, Computer simulations, Monte Carlo methods, Optoelectronics, Particle filters, Nonlinear filtering, Probability theory
At present in opto-electronic targets tracking, traditional accepted algorithms inevitably connect with linear errors. To
improve the degraded performance of general algorithms, the Adaptive Unscented Particle Kalman Filter (AUPF)
algorithm is proposed. The algorithm combines with Unscented Kalman Filter (UKF) to incorporate the most current
observation datum and to generate the importance density function. Additionally, the Markov Chain Monte Carlo
(MCMC) steps are adopted to counteract the problem of particles impoverishment caused by re-sampling step and thus
the diversity of the particles is maintained. The AUPF algorithm reduces the nonlinear influence and improves the
tracking accuracy of the opto-electronic targets tracking system. The analytic results of Monte Carlo simulation prove the
AUPF algorithm is right and feasible, and it enhances the stability, the convergence rate and the accuracy of tracking
system. The simulation results and algorithm provide a new approach for precise tracking of opto-electronic targets, they
must have better applicative prospect in various engineering than the traditional tracking algorithms.
The tracking and orientation of optoelectronic targets must obtain the data of target's velocity and angle by prediction
algorithm. But the state and measurement equations are usually nonlinear and uncoupled models, so the tracking problem
often connects with nonlinear estimation. The commonly classical extended Kalman filter (EKF) algorithm suffers from
a lot of defects. There are those problems such as easy to diverge and the convergence rate is slow and the tracking
accuracy is low. In this paper, a new nonlinear adaptive Kalman filter (AEKF) algorithm based on the adaptive tracking
theory in current statistical model is presented. It expresses variation of acceleration with the information of position and
angle to carry out self adaptation of noise variance in on-line mode, and to compensate the linear errors of model in
dynamic mode. Analytic results of Monte Carlo simulation prove the AEKF algorithm is right and feasible, and the
accuracy and the convergence rate are both improved. It has better performance than the EKF algorithm and modified
variance EKF (MVEKF) algorithm in the tracking and orientation of optoelectronic maneuvering target. The simulation
results and new method will been widely and directly applied into various engineering.