A computational geometry method for DTOA triangulation

We present a computational geometry method for the problem of triangulation in the plane using measurements of distance-differences. Compared to existing solutions to this well-studied problem, this method is: (a) computationally more efficient and adaptive in that its precision can be controlled as a function of the number of computational operations, making it suitable to low power devices, and (b) robust with respect to measurement and computational errors, and is not susceptible to numerical instabilities typical of existing linear algebraic or quadratic methods. This method employs a binary search on a distance-difference curve in the plane using a second distance- difference as the objective function. We establish the unimodality of the directional derivative of the objective function within each of a small number of suitably decomposed regions of the plane to support the binary search. The computational complexity of this method is O(log2 1/gamma), where the computed solution is guaranteed to be within a gamma-precision region centered at the actual solution. We present simulation results to compare this method with existing DTOA triangulation methods.

[1]  A. G. Mattioli,et al.  On the hyperbolic positioning of GSM mobile stations , 1998, 1998 URSI International Symposium on Signals, Systems, and Electronics. Conference Proceedings (Cat. No.98EX167).

[2]  Maurizio Spirito,et al.  Further results on GSM mobile station location , 1999 .

[3]  Leonidas J. Guibas,et al.  Kinetic data structures: a state of the art report , 1998 .

[4]  Gregory J. Pottie,et al.  Principles of Embedded Networked Systems Design: Acknowledgments , 2005 .

[5]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[6]  Ali H. Sayed,et al.  Network-based wireless location , 2005 .

[7]  R. Schmidt A New Approach to Geometry of Range Difference Location , 1972, IEEE Transactions on Aerospace and Electronic Systems.

[8]  J. Raquet,et al.  Closed-form solution for determining emitter location using time difference of arrival measurements , 2003 .

[9]  Herbert Edelsbrunner,et al.  Algorithms in Combinatorial Geometry , 1987, EATCS Monographs in Theoretical Computer Science.

[10]  Gregory J. Pottie,et al.  Principles of Embedded Networked Systems Design , 2005 .

[11]  Nageswara S. V. Rao Identification of Simple Product-Form Plumes Using Networks of Sensors With Random Errors , 2006, 2006 9th International Conference on Information Fusion.

[12]  Fredrik Gustafsson,et al.  Particle filters for positioning in wireless networks , 2002, 2002 11th European Signal Processing Conference.

[13]  H. C. Schau,et al.  Passive source localization employing intersecting spherical surfaces from time-of-arrival differences , 1987, IEEE Trans. Acoust. Speech Signal Process..

[14]  B. T. Fang,et al.  Simple solutions for hyperbolic and related position fixes , 1990 .

[15]  K. C. Ho,et al.  A simple and efficient estimator for hyperbolic location , 1994, IEEE Trans. Signal Process..

[16]  Erik G. Larsson,et al.  System performance evaluation of mobile positioning methods , 1999 .

[17]  Jennifer C. Hou,et al.  Wireless sensor networks , 2004, IEEE Wirel. Commun..

[18]  A.H. Sayed,et al.  Network-based wireless location: challenges faced in developing techniques for accurate wireless location information , 2005, IEEE Signal Processing Magazine.