Design and Performance of a Minimum-Variance Hybrid Location Algorithm Utilizing GPS and Cellular Received Signal Strength for Positioning in Dense Urban Environments

David S. De Lorenzo, Sherman C. Lo, Per K. Enge, Marty Feuerstein, Tarun K. Bhattacharya, Steve Spain, Zhengjiu Kang

Abstract:	This paper shows how the complementary advantages of GPS and cellular received signal strength (RSS) positioning methods improves hybrid location estimation performance in dense urban environments. In general, GPS techniques work best in rural or suburban environments where there is only moderate building or landscape clutter to interrupt sky visibility or to introduce multipath errors from reflected satellite signals. In contrast, cellular techniques work best in urban environments where the density and geometry of cell towers is favorable. Test results show that cellular-RSS effectively can be used to eliminate GPS outliers (location errors > 1 km, such as those associated with cell-ID fallback mode), while GPS can improve the accuracy of the combined solution. Based on field trial data, the accuracy of the Minimum-Variance Hybrid Algorithm (in dense urban environments) is approximately 45m for 67% of E911 calls and 110m for 95% of E911 calls.
Published in:	Proceedings of the 2009 International Technical Meeting of The Institute of Navigation January 26 - 28, 2009 Disney's Paradise Pier Hotel Anaheim, CA
Pages:	784 - 792
Cite this article:	De Lorenzo, David S., Lo, Sherman C., Enge, Per K., Feuerstein, Marty, Bhattacharya, Tarun K., Spain, Steve, Kang, Zhengjiu, "Design and Performance of a Minimum-Variance Hybrid Location Algorithm Utilizing GPS and Cellular Received Signal Strength for Positioning in Dense Urban Environments," Proceedings of the 2009 International Technical Meeting of The Institute of Navigation, Anaheim, CA, January 2009, pp. 784-792.
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