In this paper we present hybrid positioning schemes that combine time of arrival (TOA) and angle of arrival (AOA) measurements from only two base stations (BSs) to locate the mobile station (MS) in non-line-of-sight (NLOS) environments. The proposed methods utilize two TOA circles and two AOA lines to find all the possible intersections to locate the MS without requiring a priori information about the NLOS error. The commonly known Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and the relative positioning between the MS and the BSs greatly affects the location accuracy. The resulting geometry creates a situation where small measurement errors can lead to significant errors in the estimated MS location. Simulation results show that the proposed methods always perform better than TSA and HLOP for different levels of NLOS errors, particularly when the MS/BSs have an undesirable geometric layout.

The objective of wireless location is to determine the mobile station (MS) location in a wireless cellular communications system. When signals are propagated through non-line-of-sight (NLOS) paths, the measurements at the base stations (BSs) contain large errors which result in poor detectability of an MS by the surrounding BSs. In those situations, it is necessary to integrate all available heterogeneous measurements to improve location accuracy. This paper presents hybrid methods that combine time of arrival (TOA) at three BSs and angle of arrival (AOA) information at the serving BS to obtain a location estimate for the MS. The proposed methods mitigate the NLOS effect by using the weighted sum of the intersections between three TOA circles and the AOA line without requiring the a priori knowledge of NLOS error statistics. Numerical results show that all positioning methods offer improved estimation accuracy over those which rely on the two circles and two lines. The proposed methods always achieve better location accuracy than the Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) do, regardless of the NLOS error statistics.