; and online positioning including (21) calculating the distance from the actual measurement fingerprint lf=(rssi1, rssi2, ...rssin) to each class center, and writing as DIS=[d1, d2,...dk]; (22) finding the class corresponding to the smallest value in DIS, writing as G<SPECIAL>; (23) calculating the distance between the actual measurement fingerprint lf and each fingerprint in the G<SPECIAL>; (24) selecting the reference fingerprints; (25) calculating the weight coefficient of each reference fingerprint; and (26) calculating the position coordinate of the actual measurement fingerprint. The method clusters the RSSI value acquired in the data offline sampling phase by means of the k-mean algorithm and reduces the computational complexity of the fingerprint matching process; and in the online positioning phase, a dividing method is adopted to reduce the positioning error.
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