User Location Discovery (ULD) is a key issue in smart home ecosystems, as it plays a critical role in many applications. If a smart home management system cannot detect the actual location of the users, the desired applications may not be able to work successfully. This article proposes a new taxonomy with a broad coverage of ULD methods in terms of user satisfaction and technical features. In addition, we provide a state-of-the-art survey of ULD methods and apply our taxonomy to map these methods. Mapping contributes to gap analysis for existing ULDs and also validates the applicability and accuracy of the taxonomy. Using this systematic approach, the features and characteristics of the current ULD methods are identified (i.e., equipment and algorithms). Next, the weaknesses and advantages of these methods are analyzed utilizing ten important evaluation metrics. Although we mainly focus on smart homes, the results of this article can be generalized to other spaces such as smart offices and eHealth environments.