A Study of RSCMAC Enhanced GPS Dynamic Positioning
Description
The purpose of this research is to develop and apply the RSCMAC to enhance the dynamic accuracy of Global Positioning System (GPS). GPS devices provide services of accurate positioning, speed detection and highly precise time standard for over 98% area on the earth. The overall operation of Global Positioning System includes 24 GPS satellites in space; signal transmission that includes 2 frequency carrier waves (Link 1 and Link 2) and 2 sets random telegraphic codes (C/A code and P code), on-earth monitoring stations or client GPS receivers. Only 4 satellites utilization, the client position and its elevation can be detected rapidly. The more receivable satellites, the more accurate position can be decoded. Currently, the standard positioning accuracy of the simplified GPS receiver is greatly increased, but due to affected by the error of satellite clock, the troposphere delay and the ionosphere delay, current measurement accuracy is in the level of 5~15m. In increasing the dynamic GPS positioning accuracy, most researchers mainly use inertial navigation system (INS) and installation of other sensors or maps for the assistance. This research utilizes the RSCMAC advantages of fast learning, learning convergence assurance, solving capability of time-related dynamic system problems with the static positioning calibration structure to improve and increase the GPS dynamic accuracy. The increasing of GPS dynamic positioning accuracy can be achieved by using RSCMAC system with GPS receivers collecting dynamic error data for the error prediction and follows by using the predicted error to correct the GPS dynamic positioning data. The ultimate purpose of this research is to improve the dynamic positioning error of cheap GPS receivers and the economic benefits will be enhanced while the accuracy is increased.
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References
- Albus, J. S., "A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)," Journal of Dynamic Systems, Measurement, and Control, Transactions of ASME, p.220-227, September 1975.
- Albus, J. S., "Data Storage in the Cerebellar Model Articulation Controller (CMAC)," Journal of Dynamic Systems, Measurement, and Control, Transactions of ASME, p.228-233, September 1975.
- Ching-Tsan Chiang, Yu-Bin Lin and Chia-Yen Hsieh, "Hardware Implementation of Recurrent S_CMAC_GBF Based on FPGA", 2008 International Conference on Machine Learning and Cybernetics, Kunming, China, Vol. 7, pp. 3845-3850, 12-15, July, 2008.
- M. R. Mosavi, K. Mohammadi, and M. H. Refan, "A New Approach for Improving of GPS Positioning Accuracy by using an Adaptive Neurofuzzy System, before and after S/A Is Turned off", International Journal of Engineering Science, Iran University of Science and Technology, vol. 15, pp. 101-114, 2004.
- M. R. Mosavi, "Comparing DGPS Corrections Prediction using Neural Network, Fuzzy Neural Network, and Kalman Filter", Journal of GPS Solution, pp. 1-11, 2005.
- M. R. Mosavi, "A Comparative Study between Performance of Recurrent Neural Network and Kalman Filter for DGPS Correction Prediction", The 7th IEEE Conference on Signal Processing, September 2004.
- F. Ibrahim and A. Tascillo, "DGPS/INS Integration using Neural Network Methodology", The 12th IEEE International Conference on Tools with Artificial Intelligence, pp. 114-121, 2000.
- Mosavi M. R., "Comparing DGPS Corrections Prediction using Neural Network, Fuzzy Neural Network, and Kalman Filter", Journal of GPS Solutions, 10(2), pp.97-107, 2006.
- Y. Otsuka, T. Ogawa, A. Saito, T. Tsugawa, S. Fukao, S. Miyazaki, "A new technique for mapping of total electron content using GPS network in Japan", Earth Planets Space, (54) 63-70, 2002. [10] Arikan, F., Erol, C.B., Arikan, O., "Regularized Estimation of TEC from GPS Data for Certain Midlatitude Stations and Comparisons with IRI Model", J. Adv. Space Res., doi:10.1016/j.asr.2007.01.082, 2007. [11] L. Zhao, W. Y. Ochieng, M. A. Quddus, and R. B. Noland, "An extended Kalman filter algorithm for integrating GPS and low-cost dead reckoning system data for vehicle performance and emissions monitoring," J. Navig., vol. 56, no. 2, pp. 257-275, 2003. [12] R. Sharaf, M. Tarbouchi, A. El-Shafie, and A. Noureldin, "Real-time implementation of INS/GPS data fusion utilizing adaptive neuro-fuzzy inference system," presented at the Inst. Nav. (ION) Nat. Tech. Meeting, Jan. 24-26, 2005. [13] S. Nassar, A. Noureldin, and N. El-Sheimy, "Improving positioning accuracy during kinematic DGPS outage periods using SINS/DGPS integration and SINS data de-noising," Survey Rev., vol. 37, no. 292, pp. 426-438, Apr. 2004. [14] R. Sharaf, A. Noureldin, A. Osman, and N. El-Sheimy, "Online INS/GPS integration with a radial basis function neural network," IEEE Aerosp. Electron. Syst. Mag., vol. 20, no. 3, pp. 8-14, Mar. 2005. [15] Ching-Tsan Chiang, Jih-Sheng Hsu, and Sheng-Jie Yang, "Hardware Implementation in DGPS Accuracy Improvement by Using RSCMAC. 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), Shanghai, China, Oct, 2011.