Inspired by promotion of commercial applications, support of location-based services to mobile terminals through their current location has been receiving a lot of attention in recent years even though emergency communications is the primary motivation for development of wireless location. A major challenge to wireless location technique is how to balance the implementation complexity and required accuracy. In the first part of this dissertation, we address one of the fundamental problems in wireless location when using the ToA measurements and develop a simple model to estimate the mobile terminal location with low complexity and promising accuracy. The model employs the geometrical transformation method with single propagation delay measurement. The contribution is that the use of geometrical transformation allows us to overcome the location handover problem, i.e., a forcing handover in a GSM (global system for mobile) network or a three-way soft handover in a UMTS (universal mobile telecommunications system) network. By using the proposed location model, the impact on network performance is kept at the minimum level and the complexity and requirements for hardware and software changes are reduced. In the second part of this dissertation, we address one of the fundamental problems in wireless location when using the SS (signal strength) measurements. The first contribution is to develop a novel wireless location technique based on a ¡§differ- encing¡¨ way, called the SSSD (stationary signal-strength-difference), to remove the uncertainty propagation parameters when merging environment-dependent signal propagation model into the location estimation. This is due to the uncertainty in propagation parameters causes a propagation model error that enlarges error in the distance estimation. The performance gained from the preliminary analysis of SSSD location technique, however, is degraded as a result of the large bias error in the estimated distance and distance difference. To achieve the performance enhancement, the second contribution is to correct the bias error in the estimated distance difference by using a correction method based on a geometric constraint condition. With the corrected distance difference, the final contribution is that we generalized the work on correction method and provide a new framework to correct the error in the estimated distance. As the corrected distance and distance difference is derived by LS (least square) computation, respectively, low computation burden and non-iterative solutions were achieved. To the best of our knowledge thus far, this is first such proposal for a correction to the SS-based location technique. It is demonstrated that the proposed error correction method is shown to perform well when encountering the large error in the estimated distance and distance difference, and prove that the location accuracy can be improved considerably.