Abstract
Recently, the location of the fingerprint positioning technology is obviously superior to the signal transmission loss model based on the positioning technology, and is widely concerned by scholars. In the online phase, due to the efficiency of the probabilistic distribution matching computation is low and when clustering the position fingerprint database, hard clustering lead to degrading the positioning accuracy, a probabilistic algorithm based on fuzzy clustering is proposed and applied to the indoor location fingerprinting positioning. Compared with hard clustering fusion algorithm, the proposed method has realized the fuzzy partition of the database, makes online positioning phase can effectively search the desired fingerprint data, and improve the positioning accuracy. Experiments show that the algorithm can effectively deal with the problem of the positioning accuracy of hard clustering.
Highlights
With the development of wireless network technology, the demand for location-based services is increasing [1]
Indoor positioning algorithm consists of Arrival of Angle (AOA) positioning method and Time of Arrival (TOA) positioning method, Time Difference of Arrival (TDOA) positioning method and positioning method based on signal strength (RSSI) [3]
The location fingerprint positioning method based on the signal strength becomes the main research direction in the locating method based on signal strength [8].The main process of the location fingerprinting positioning method can be divided into two phases: the offline phase and the online phase [9]
Summary
The location of the fingerprinting position method based on the signal strength is divided into two phases: offline and online phases [9]. 3) Signal receiving device placed on the reference point location collect the wireless signal from the AP. In the online positioning phase, the mobile terminal collects the AP signal strength information, and the fingerprint matching algorithm is used to find the best match with the fingerprint data constructed in the offline phase, and obtains user location information of the mobile terminal. The algorithm selects the appropriate class family based on signal strength acquisition, and uses probabilistic method to obtain the coordinates of the target point, in order to achieve precise positioning location.
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