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Location fingerprinting provides localization for devices in indoor environments using existing Wireless Local Area Network (WLAN) infrastructure. However, the initial offline calibration which is required for these types of systems is non-trivial and requires significant labour cost. The current practice is to collect measurements at calibration points in a uniform grid for the area which is covered by the radio map. However, not all calibration points are resolvable in signal space and addition of unresolvable calibration points does not improve the localization accuracy. This paper presents Spatial Aware Signal Space Clustering (S3C) clustering algorithm which analyses walk test data for identifying these unresolvable calibration points prior to calibration phase. Simulation based studies shows that the algorithm is able to reduce the labour cost of calibration phase while preserving the localization accuracy.

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