Calculation of Weighted Geometric Dilution of Precision

To achieve high accuracy in wireless positioning systems, both accurate measurements and good geometric relationship between the mobile device and the measurement units are required. Geometric dilution of precision (GDOP) is widely used as a criterion for selecting measurement units, since it represents the geometric effect on the relationship between measurement error and positioning determination error. In the calculation of GDOP value, the maximum volume method does not necessarily guarantee the selection of the optimal four measurement units with minimum GDOP. The conventional matrix inversion method for GDOP calculation demands a large amount of operation and causes high power consumption. To select the subset of the most appropriate location measurement units which give the minimum positioning error, we need to consider not only the GDOP effect but also the error statistics property. In this paper, we employ the weighted GDOP (WGDOP), instead of GDOP, to select measurement units so as to improve the accuracy of location. The handheld global positioning system (GPS) devices and mobile phones with GPS chips can merely provide limited calculation ability and power capacity. Therefore, it is very imperative to obtain WGDOP accurately and efficiently. This paper proposed two formations of WGDOP with less computation when four measurements are available for location purposes. The proposed formulae can reduce the computational complexity required for computing the matrix inversion. The simpler WGDOP formulae for both the 2D and the 3D location estimation, without inverting a matrix, can be applied not only to GPS but also to wireless sensor networks (WSN) and cellular communication systems. Furthermore, the proposed formulae are able to provide precise solution of WGDOP calculation without incurring any approximation error.

[1]  Saipradeep Venkatraman,et al.  A novel ToA location algorithm using LoS range estimation for NLoS environments , 2004, IEEE Transactions on Vehicular Technology.

[2]  Jium-Ming Lin,et al.  Neural Network for WGDOP Approximation and Mobile Location , 2013 .

[3]  Gordon L. Stüber,et al.  Subscriber location in CDMA cellular networks , 1998 .

[4]  Yang Yong,et al.  GDOP results in all-in-view positioning and in four optimum satellites positioning with GPS PRN codes ranging , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[5]  K.J.R. Liu,et al.  Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs , 2005, IEEE Signal Processing Magazine.

[6]  David Akopian,et al.  Weighted dilution of precision as quality measure in satellite positioning , 2003 .

[7]  K. Kawamura,et al.  Study on the improvement of measurement accuracy in GPS , 2006, 2006 SICE-ICASE International Joint Conference.

[8]  Dah-Jing Jwo,et al.  Applying Back-propagation Neural Networks to GDOP Approximation , 2002, Journal of Navigation.

[9]  Szu-Lin Su,et al.  Resilient Back-propagation Neural Network for Approximation 2-D GDOP , 2010 .

[10]  J. J. Caffery,et al.  A new approach to the geometry of TOA location , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[11]  Tsuyoshi Okada,et al.  A Satellite Selection Method and Accuracy for the Global Positioning System , 1984 .

[12]  Chansik Park,et al.  A satellite selection criterion incorporating the effect of elevation angle in GPS positioning , 1996 .

[13]  Lu,et al.  Compass Augmented Regional Constellation Optimization by a Multi-objective Algorithm Based on Decomposition and PSO , 2012 .

[14]  George M. Siouris,et al.  Aerospace Avionics Systems: A Modern Synthesis , 1993 .

[15]  Jijie Zhu,et al.  Calculation of geometric dilution of precision , 1992 .

[16]  Michael J. Rycroft,et al.  Understanding GPS. Principles and Applications , 1997 .

[17]  Sun Zhongkang,et al.  A nonlinear optimized location algorithm for bistatic radar system , 1995, Proceedings of the IEEE 1995 National Aerospace and Electronics Conference. NAECON 1995.

[18]  Xu Bo,et al.  Satellite selection algorithm for combined GPS-Galileo navigation receiver , 2000, 2009 4th International Conference on Autonomous Robots and Agents.

[19]  Dan Simon,et al.  Navigation satellite selection using neural networks , 1995, Neurocomputing.

[20]  Dan Simon,et al.  Fault-tolerant training for optimal interpolative nets , 1995, IEEE Trans. Neural Networks.

[21]  M. Kihara,et al.  Study of a GPS satellite selection policy to improve positioning accuracy , 1994, Proceedings of 1994 IEEE Position, Location and Navigation Symposium - PLANS'94.

[22]  Zhang Jun,et al.  Satellite selection for multi-constellation , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[23]  Szu-Lin Su,et al.  Hybrid TOA/AOA Geometrical Positioning Schemes for Mobile Location , 2009, IEICE Trans. Commun..

[24]  M. Pachter,et al.  Accurate positioning using a planar pseudolite array , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[25]  N. Levanon Lowest GDOP in 2-D scenarios , 2000 .

[26]  D.Y. Hsu,et al.  Relations between dilutions of precision and volume of the tetrahedron formed by four satellites , 1994, Proceedings of 1994 IEEE Position, Location and Navigation Symposium - PLANS'94.

[27]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.