I-WKNN: Fast-Speed and High-Accuracy WIFI Positioning for Intelligent Stadiums

TL;DR

This paper introduces I-WKNN, a high-precision, fast indoor positioning algorithm for stadiums that outperforms traditional methods in accuracy and speed, especially in noisy environments.

Contribution

The paper proposes a novel I-WKNN algorithm with an AP selection method and asymmetric Gaussian filtering tailored for complex stadium environments.

Findings

I-WKNN improves positioning accuracy over traditional WKNN and KNN.

The algorithm demonstrates faster real-time positioning in noisy environments.

Experimental results confirm its suitability for smart stadium applications.

Abstract

Based on various existing wireless fingerprint location algorithms in intelligent sports venues, a high-precision and fast indoor location algorithm improved weighted k-nearest neighbor (I-WKNN) is proposed. In order to meet the complex environment of sports venues and the demand of high-speed sampling, this paper proposes an AP selection algorithm for offline and online stages. Based on the characteristics of the signal intensity distribution in intelligent venues, an asymmetric Gaussian filter algorithm is proposed. This paper introduces the application of the positioning algorithm in the intelligent stadium system, and completes the data acquisition and real-time positioning of the stadium. Compared with traditional WKNN and KNN algorithms, the I-WKNN algorithm has advantages in fingerprint positioning database processing, environmental noise adaptability, real-time positioning accuracy and positioning speed, etc. The experimental results show that the I-WKNN algorithm has obvious advantages in positioning accuracy and positioning time in a complex noise environment and has obvious application potential in a smart stadium.