Radio-based human tracking for large indoor environments using distributed centroid location estimation

Common received signal strength (RSS) indoor local positioning systems (ILPS) are based on a distributed collection of sensor data using reference infrastructure components (reference node, beacon node) and a centralized position estimation. The maximum number of mobile blind nodes (BNs) which can be located simultaneously and the coverage of the system are limited due to the channel capacity of the backbone (wired or wireless) for the sensor data collection. To overcome the bus load issue for wired systems, a hierarchical bus structure with multiple gateway segments is proposed. A data compression using a distributed weighted centroid localization (dWCL) strategy with precomputed gateway segment estimations is implemented to extend the coverage of the localization system. 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 10 while the location estimation error increases only slightly.