The Method of Enhanced Gaussian Function Weighted KNN Indoor Positioning

doi:10.13474/j.cnki.11-2246.2017.0179

Abstract

Abstract: Because of less deployed APs in some indoor areas and signal fingerprint time-varying characteristics, it is possible for the currently scanning RSSI vector to be similar to corresponding RSSI sequence which is stored with location in radio map. In these cases, the calculated Euclidean distance is usually 0 or very small. Error will occur when the Euclidean distance is used for weight value in weighted centroid algorithm, and no result will be obtained. And KNN algorithm, which supposes the value 1/K as weight, will get the average of coordinates of K reference points along with relative low positioning accuracy. Therefore, Gaussian weighted KNN (GWKNN) localization algorithm is proposed:standardization processes for K nearest Euclidean distances were made, then corresponding weights were distributed by Gaussian function, at last, the weighted positioning result is obtained. Compared with the positioning results of KNN and WKNN algorithm, this positioning method can get higher robustness and positioning accuracy.

Key words: RSSI, Euclidean distance, Gaussian function, assign weights, KNN, indoor positioning

CLC Number:

P228

BI Jingxue, ZHEN Jie, WANG Yunjia, LIU Xiaoxiao. The Method of Enhanced Gaussian Function Weighted KNN Indoor Positioning[J]. 测绘通报, 2017, 0(6): 9-12,35.

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