Mobile Position Estimation based on Three Angles of Arrival using an Interpolative Neural Network

Abstract

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperforms the two other methods in its estimations for different noise conditions.

References

• Karray, F. O. and Silva, C. D. (2004) Soft Computing and Intelligent Systems Design. England: Pearson education.
• SHEN, X. MARK, J. W. and YE, J. (2002) Mobile Location Estimation in CDMA Cellular Networks by Using Fuzzy Logic. Wireless Personal Communications. [online] vol. 22(1). P. 57-70. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Wann, C. and Chen, Y. (2002) Position Tracking and Velocity Estimation for Mobile Positioning Systems. In- Wireless Personal Multimedia Communications. vol. 1. p. 310-314. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Caffery, J. and St¨uber, G. L. (1998) Subscriber Location in CDMA Cellular Networks. IEEE Transactions on Vehicular Technology. [online] vol. 47(2/May). P. 406-416. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Wei, K. and Lenan, W. (2009) Constrained Least Squares Algorithm for TOA-Based Mobile Location under NLOS Environments. In- 5th International Conference on Wireless Communications, Networking and Mobile Computing. P. 1-4. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Yang, C. , Chen, B. and Liao, F. (2010) Mobile Location Estimation Using Fuzzy-Based IMM and Data Fusion. IEEE Transactions on Mobile Computing. [online] vol. 9(10/October). p. 1424-1436. Available from: www. ivsl. org [Accessed: 10th April 2014].
• LiuYing, Liang,Y. , and Wang, S. (2000) Location Parameters Estimation in Mobile Communication Systems. In- Communication Technology proceeding. vol. 1. p. 261-268. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Voltz, P. J. and Hernandez, D. (2004) Maximum Likelihood Time of Arrival Estimation for Real-Time Physical Location Tracking of 802. 1 1 a/g Mobile Stations in Indoor Environments. In- Position Location and Navigation Symposium. p. 585-591. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Chen, C. and Feng, K. (2005) Hybrid Location Estimation and Tracking System for Mobile Devices. In- IEEE 61st Vehicular Technology Conference. vol. 4. p. 2648-2652. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Zhou, J. , Chu, K. M. , and Ng, J. K. (2009) A Probabilistic Approach to Mobile Location Estimation within Cellular Networks. In- 15th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications. P. 341-348. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Chen, C. , Su, S. , and Lu, C. (2010) Geometrical Positioning Approached for Mobile Location Estimation. In- 2nd IEEE International Conference on Information Management and Engineering. p. 268-272. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Cong, L. and Zhuang, W. (2002) Hybrid TDOA/AOA Mobile User Location for Wideband CDMA Cellular Systems. IEEE Transactions on Wireless Communications. [online] vol. 1(3/July). p. 439-447. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Venkatraman, S. , Caffery, J. and You, H. (2004) A Novel ToA Location Algorithm Using LoS Range Estimation for NLoS Environments. IEEE Transactions on Vehicular Technology. [online] vol. 53(5/September). p. 1515-1524. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Lin, D. and Juang, R. (2005). Mobile Location Estimation Based on Differences of Signal Attenuations for GSM Systems. IEEE Transactions on Vehicular Technology. [online] vol. 54(4/July). p. 1447-1454. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Chen, C. and Lin, J. (2011) Applying Rprop Neural Network for the Prediction of the Mobile Station Location. Sensors. [online] vol. 11(4). p. 4207-4230. Available from: www. ivsl. org [Accessed: 10th April 2014].
• Landolsi, M. A. , Muqaibel, A. H. , Al-Ahmari, A. S. , Khan, H. -R. and Al-Nimnim, R. A. (2010). Performance Analysis of Time-of-Arrival Mobile Positioning in Wireless Cellular CDMA Networks. In- Bouras, C. J. (ed. ). Trends in Telecommunications Technologies. [online] Available from: http://www. intechopen. com/books/trends-in-telecommunications-technologies/performance-analysis-of-time-of-arrival-mobile-positioning-in-wireless-cellular-cdma-networks. [Accessed: 10th April 2014].
• Liu, H. Darabi, H. Banerjee, P. and Liu, J. (2007) Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews. [online] vol. 37(6/November). Available from: www. ivsl. org [Accessed: 10th April 2014].

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