The emergence of innovative location-oriented services and the great advances in mobile computing and wireless networking motivated the development of positioning systems in indoor environments. However, despite the benefits from location awareness within a building, the implicating indoor characteristics and increased user mobility impeded the implementation of accurate and time-efficient indoor localizers. In this paper, we consider the case of indoor positioning based on the correlation between location and signal intensity of the received Wi-Fi signals. This is due to the wide availability of WLAN infrastructure and the ease of obtaining such signal strength (SS) measurements by standard 802.11 cards. With our focus on the radio scene analysis (or fingerprinting) positioning method, we study both deterministic and probabilistic schemes. We then describe techniques to improve their accuracy without increasing considerably the processing time and hardware requirements of the system. More precisely, we first propose considering orientation information and simple SS sample processing during the training of the system or the entire localization process. For dealing with the expanded search space after adding orientation-sensitive information, we suggest a hierarchical pattern matching method during the real-time localization phase. Numerical results based on real experimental measurements demonstrated a noticeable performance enhancement, especially for the deterministic case which has additionally the advantage of being less complex compared to the probabilistic one.