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Andreas Fink, Helmut Beikirch


The prevalent evaluation criterion for indoor local positioning systems (ILPS) is the achievable accuracy in terms of Euclidean distance between estimated and true position. Systems relying on received signal strength (RSS) ranging often use a distributed collection of RSS sensor data at reference nodes and a centralized position estimation. For this direct remote positioning, the accuracy is dependent on the reference node density and thus, is indirect proportional to the achievable coverage. To split up the dependency between these two criteria, we propose a distributed weighted centroid localization (dWCL) strategy with a hierarchical sensor data field bus. Accuracy and coverage of centralized and distributed WCL algorithms are compared for a one-dimensional tracking simulation and 196 reference nodes, arranged in up to 28 gateway segments. Using distributed computations, the localization system’s coverage is increased by factor ten while the location estimation error increases only slightly.


Centroid Localization; Distributed Computing; Human Tracking; RSS Ranging.

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