Multi-fingerprint fusion and area refinement for WiFi indoor positioning methods
Aiming at the problems of poor robustness and low accuracy in location determination caused by the representation of positional information with a single fingerprint feature in complex environments,the paper proposed a wireless fidelity (WiFi) indoor positioning method based on the fusion of multiple fingerprint features and area refinement:the feature selection (ReliefF) algorithm was improved to determine the contributions of four single fingerprint features,including received signal strength indicator (RSSI),signal change rate (Rate),hyperbolic location fingerprint (HLF) and signal strength difference (SSD),to positional information;and a weighted fusion of these four single fingerprint features was performed to obtain a combined positional feature;then,an area refinement algorithm based on the rate of change in combined positional feature data and the k-means algorithm was put forward,which refines the positioning area during the offline construction of the fingerprint database.Experimental results showed that the WiFi positioning method based on multi-fingerprint fusion and area refinement could offer higher positioning accuracy,speed and robustness compared to the WiFi positioning methods using a single feature.
万方数据
维普
