A New Weighted Algorithm Based on the Uneven Spatial Resolution of RSSI for Indoor Localization | Zendy

Weixing Xue | Zendy; Xianghong Hua | Zendy; Qingquan Li | Zendy; Kegen Yu | Zendy; Weining Qiu | Zendy; Baoding Zhou | Zendy; Kai Cheng | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A New Weighted Algorithm Based on the Uneven Spatial Resolution of RSSI for Indoor Localization

Author(s) -

Weixing Xue,

Xianghong Hua,

Qingquan Li,

Kegen Yu,

Weining Qiu,

Baoding Zhou,

Kai Cheng

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2837018

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

The weighted K-nearest neighbor (WKNN) algorithm is one of the most frequently used algorithms for indoor positioning. However, the traditional WKNN algorithm weights the reference points’ coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of the Wi-Fi RSSI. Therefore, in order to improve the positioning accuracy of traditional location algorithms, this paper proposes a new weighted algorithm based on the physical distance of the RSSI. Experiments were conducted in an office building and the results demonstrate that the proposed method considerably outperforms the KNN, Euclidian-W-KNN, Manhattan-W-KNN, EWKNN, LiFS, and GPR in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research