Speeding is one of the leading causes of traffic crashes worldwide. Radar velocimeter is widely used in the capture monitoring of road overspeed violations, which can effectively reduce the probability of traffic accidents and protect people’s life and property safety to the greatest extent. As a new type of radar velocimeter, multitarget radar velocimeter (MTRV) can monitor the speed of more than two vehicles at the same time. However, the verification method and device of MTRV’s performance need to be studied. In order to solve the problem of performance verification for MTRV, a verification device based on echo signal simulation technology is developed in this paper. The measurement mechanism of MTRV with different performance including velocity, distance, and angle is first introduced. Then, a verification method based on the echo signal simulation technology is proposed. The verification device can receive the emission signal of MTRV and process the signal by echo simulation technology, including target generation, Doppler frequency shift, time delay, and angel control, and targets are simulated with nominal velocity, distance, and angle value. The processed echo signal with simulated nominal parameter values is reflected to the MTRV. After the echo signal is received and processed by MTRV, the measurement values of simulated velocity, distance, and angle for targets are obtained. Comparing the measured values of the MTRV with the simulated nominal values of the verification device, the measurement error of MTRV is obtained. The verification device of MTRV is realized to verify the accuracy and reliability of the MTRV measurement results. The simulated velocity range of the verification device is up to (-300~300) km/h, and the simulated distance range of the verification device is up to (10~45) m when the simulated incident angle range was within the range of (-60~60)°. The simulation target generation for the two targets of the device is also verified. And the maximum permissible error (MPE) of the simulated velocity was ±0.05 km/h, the MPE of simulated distance is ±0.3 m, and the MPE of simulated angle is ±0.2°. Finally, the verification and uncertainty evaluation results of the MTRV sample validated the effectiveness and feasibility of the proposed verification method and the developed verification device of MTRV.
This article developed an improved statistical pattern analysis (SPA) monitoring strategy for fault detection of complex multivariate processes using empirical likelihood. The technique based on statistical pattern analysis performs fault detection by inspecting change in the statistics of process variables (e.g., mean value, correlation coefficient, variance, kurtosis, etc.). It is capable of monitoring non-Gaussian or even nonlinear processes. However, the original SPA framework explicitly computes all the high-order statistics, which significantly increases the scale and dimensionality of the problem, especially in the case of complex multivariate processes. To alleviate this difficulty, we propose monitoring changes in the statistics with the same order using empirical likelihood, which is a widely used estimation method to construct confidence limits or regions for parameters with similar properties. As a result, changes in statistics of the same order can be translated into a single index; hence more information on the faulty conditions can be observed. Furthermore, by considering statistics of the same order, the scale of the problem is reduced significantly. The improved statistical pattern analysis monitoring strategy is suitable for monitoring complex multivariate processes. The performance of the improved method is illustrated by an application study to fault detection of the Tennessee Eastman (TE) process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.