An Improved Fingerprint Indoor Localization Algorithm Based on Combination Weight

doi:10.13474/j.cnki.11-2246.2018.0034

Abstract

Abstract:

Under the influence of complex indoor scene,unstable WiFi signal and other factors,there are some mismatches in neighbor points selecting based on the algorithm of K-nearest.For these mismatch errors,there will be direct impact on the results of localization.In this paper,we proposed an improved fingerprint indoor localization algorithm based on combination weight.Firstly,analyze the geometrical structures of K-nearest points,and then remove the points which have the longest distance between the center of K-nearest points and itself.Secondly,analyze the geometrical location of unknown point and the center of K-nearest points,and use the geometrical distance between neighbor points and the center of them,the Euclidean distance between neighbor points and the unknown point to determine the weight of algorithm collectively.Finally,obtain the weighted localization results.Compared with the localization results of KNN and WKNN algorithm,the improved algorithm gets higher localization accuracy and robustness.

Key words: indoor localization, WiFi fingerprint, WKNN, structure of neighbor points, selection of neighbor points, combination weight

CLC Number:

P228

CAO Xiaoxiang, CHEN Guoliang. An Improved Fingerprint Indoor Localization Algorithm Based on Combination Weight[J]. 测绘通报, 2018, 0(2): 6-10.

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