In ubiquitous computing, localization of users in indoor environments is a challenging issue. On the one hand, localization data needs to have fine granularity to provide reasonable input for intention recognition and task planning. On the other hand, effects like multi-path interference and signal scattering of RF propagation in indoor environments reduces the accuracy of traditional wireless localization techniques. However, we prove that such adversary effects can be characterized and utilized conversely to localize the source of RF-scatter with passive UHF RFID. Since measuring spatial correlation requires many spatially separated transmitter and receiver pairs, cost-effective and unobtrusively attachable passive RFID-tags are especially suitable for this purpose. The passive tags are spatially distributed in a manner such that it is possible to infer the spatial correlation of Received Signal Strength (RSS). The idea is to characterize the influence of user presence on RSS, and use such relationship for localization. Three localization algorithms are investigated which consist of a Maximum Likelihood Estimator (MLE), and two Linear Least Squares variants. Algorithms are applied to measurement data which we obtained in an indoor environment. The results evidences our idea of human localization in such bistatic RFID systems.