Accuracy Improvement of Accelerometer-Based Location Estimation Using Neural Network

2018 ◽  
pp. 79-85
Author(s):  
Noritaka Shigei ◽  
Hiroki Urakawa ◽  
Yoshihiro Nakamura ◽  
Masahiro Teramura ◽  
Hiromi Miyajima
Keyword(s):  
Neural Network ◽  
Location Estimation ◽  
Accuracy Improvement

scholarly journals Neural Network for WGDOP Approximation and Mobile Location

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chien-Sheng Chen ◽  
Jium-Ming Lin ◽  
Chin-Tan Lee
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Location Estimation ◽  
Estimation Accuracy ◽  
Neural Network Learning ◽  
Mobile Location ◽  
Positioning Systems ◽  
Geometric Dilution Of Precision ◽  
Location Methods

This paper considers location methods that are applicable in global positioning systems (GPS), wireless sensor networks (WSN), and cellular communication systems. The approach is to employ the resilient backpropagation (Rprop), an artificial neural network learning algorithm, to compute weighted geometric dilution of precision (WGDOP), which represents the geometric effect on the relationship between measurement error and positioning error. The original four kinds of input-output mapping based on BPNN for GDOP calculation are extended to WGDOP based on Rprop. In addition, we propose two novel Rprop–based architectures to approximate WGDOP. To further reduce the complexity of our approach, the first is to select the serving BS and then combines it with three other measurements to estimate MS location. As such, the number of subsets is reduced greatly without compromising the location estimation accuracy. We further employed another Rprop that takes the higher precision MS locations of the first several minimum WGDOPs as the inputs into consideration to determine the final MS location estimation. This method can not only eliminate the poor geometry effects but also significantly improve the location accuracy.

scholarly journals Artificial Neural Network Approach to Mobile Location Estimation in GSM Network

2017 ◽  
Vol 63 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Longinus S. Ezema ◽  
Cosmas I. Ani
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Performance Metrics ◽  
Federal Communication Commission ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Accuracy ◽  
Neural Network Approach ◽  
Mobile Location ◽  
Gsm Network

AbstractThe increase in utilisation of mobile location-based services for commercial, safety and security purposes among others are the key drivers for improving location estimation accuracy to better serve those purposes. This paper proposes the application of Levenberg Marquardt training algorithm on new robust multilayered perceptron neural network architecture for mobile positioning fitting for the urban area in the considered GSM network using received signal strength (RSS). The key performance metrics such as accuracy, cost, reliability and coverage are the major points considered in this paper. The technique was evaluated using real data from field measurement and the results obtained proved the proposed model provides a practical positioning that meet Federal Communication Commission (FCC) accuracy requirement.

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