The Research of Weighted-Average Fusion Method in Inland Traffic Flow Detection
Inland waterway traffic flow statistical data is an important foundation for water transportation planning, construction, management, maintenance and safety monitoring. Proposing a multi-sensor data fusion algorithm based on the weighted average estimation method is used to deal with the vessel traffic flow data, and the optimal weight ratio is inducted. Data fusion method is on the basis of weighted average estimation theory, using distributing map of detection technology to test the consistency of data, checking the data to exclude abnormal ones and record missing data, fusing effective data to improve data accuracy. With MATLAB simulation, this example show that the weighed average estimate data fusion method is simple, with high reliability, can effectively improve the robustness of the system measurements, it can get accurate test results. For inland river ships traffic flow testing various sensing device for the collected data format is not the same, weighted average estimate data fusion method is suitable for the situation.
Keywordstraffic flow detection multisensor data fusion weighted average
Unable to display preview. Download preview PDF.
- 1.Yan, Z., Yan, X., Ma, F., et al.: Green Yangtze River, Intelligent Shipping Information System and Its Key Technologies. Journal of Transport Information and Safety, 76–81 (2010)Google Scholar
- 2.Yan, X., Ma, F., Chu, X., et al.: Key Technology of Collecting Traffic Flow on the Yangtze River in Real-Time. Navigation of China, 40–45 (2010)Google Scholar
- 3.Bataillou, E., Thierry, E., Rix, H.: Weighted averaging with adaptive weight estimation. Computers in Cardiology, Proceedings, 37–40 (1991)Google Scholar
- 4.Fu, H., Du, X.: Multi-sensor Optimum Fusion Based on the Bayes Estimation. Techniques of Automation and Applications, 10–12 (2005)Google Scholar
- 5.Liu, H., Gong, F.: An Improved Weighted Fusion Algorithm of Multi-sensor. Electronic Engineering & Product World, 19–21 (2009)Google Scholar
- 6.Shozo, M., Barker, W.H., Chong, C.Y.: Track association track fusion with nondeterministic dynamic target. IEEE Trans. on Aerospace and Electronic System, 659–668 (2002)Google Scholar
- 7.Spalding, J., Shea, K., Lewandowisk, M.: Intelligent Waterway System and the Waterway Information Network. The Institute of Navigation National Technical Meeting, 484–487 (2002)Google Scholar
- 8.Pilcher, C., Khotanzad, A.: Nonlinear Classifier Combination for a Maritime Target Recognition Task. In: IEEE 2009 Radar Conference, pp. 873–877. IEEE Press, California (2009)Google Scholar
- 9.Zhang, M., Hu, J., Zhou, Y.-f.: Research of Improved Particle Filtering Algorithm. Ordnance Industry Automation, 61–63 (2008)Google Scholar
- 10.Zhang, J., Wang, W., Wu, Q.: Federated Kalman Filter Based on Optoelectronic Tracking System. Semiconductor Optoelectronics, 602–605 (2008)Google Scholar