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Tracking a moving target is a difficult task in a distributed sensor network due to the lack of knowledge of the target's motion and signal noises. Several existing approaches use only sensory information or may require accurate target's motion models. In this paper, we present a Markovian approach that combines dynamically estimated target's motion models with received sensory information. The approach localizes a target by using the estimated motion models and the provided sensory model. We characterize probabilistic conditions under which the estimation accuracy increases if more sensors are used, and the estimations converge to the target's real position asymptotically. Our experimental analysis shows that our approach leads to substantially more accurate and robust location estimations than the previous approaches using only sensory information, and it is competitive with the standard Markov localization approach.