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The increasing availability of ubiquitous, small, low-cost devices using wireless communications to build wireless sensor networks calls for autonomous solutions and algorithms capable of calculating the location where the information is gathered, processed, used. The requirements are particularly strict when sensor nodes are mobile; in fact, mobile applications demand more accurate locating and real time tracking, with limited impact on hardware complexity, network load and latency. Despite of remarkable research efforts put into this field by the scientific community, a common unique solution has not been adopted yet, due to the great variety of scenarios and application requirements. This paper focuses on the design and performance evaluation of Kalman filters for tracking a mobile target moving at low dynamics and for smoothing range estimations from noisy measurements. Some adaptive techniques to self-tune the filter and estimate the propagation model parameters are presented. One among the most promising adaptive algorithm presented is implemented in a real-life wireless sensor network test-bed including a mobile node. The resulting experimental results illustrate the benefits of this approach with respect to traditional multilateration based on least mean squares estimators.