Indoor Location Optimization of Wireless Sensor Coverage Based on KWAP-KNN Partition Clustering Algorithm
The partition clustering algorithm is used in indoor localization and has good localization results.In order to improve the indoor positioning accuracy of wireless sensing coverage,a clustering algorithm based on KWAP-KNN is proposed based on the analysis of indoor positioning signals based on Wi-Fi,and the experimental test analysis is carried out.When partitioning,k-means is selected,and K clustering centers are used as the loca-tion of the signal transmitter.When dividing cross areas and uncovered areas,clustering is adopted,and KNN clustering is adopted in small areas.The experimental results show that WAP clustering algorithm is lower than AP clustering algorithm in terms of similarity between sample points and clustering time,and the clustering effect is better.The research has a good practical significance for improving the indoor positioning accuracy of wireless sen-sor coverage.
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