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Wireless Sensor Networks are well suited for tracking targets carrying RFID tags in indoor environments. Tracking based on the received signal strength indication (RSSI) is by far the cheapest and simplest option, but suffers from secular biases due to effects of multi-path, occlusions and decalibration, as well as large unbiased errors due to measurement noise. We propose a novel algorithm that solves these problems in a distributed, scalable and power-efficient manner. Firstly, our proposal includes a tandem incremental estimator that learns and tracks the radio environment of the network, and provides this knowledge for the use of the tracking algorithm, which eliminates the secular biases due to radio occlusions etc. Secondly, we reduce the unbiased tracking error by exploiting the co-dependencies in the motion of several targets (as in crowds or herds) via a fully distributed and tractable particle filter. We thereby extract a significant 'diversity gain' while still allowing the network to scale seamlessly to a large tracking area. In particular, we avoid the pitfalls of network congestion and severely shortened battery lifetimes that plague procedures based on the joint multi-target probability density.