BiCSI: A Binary Encoding and Fingerprint-Based Matching Algorithm for Wi-Fi Indoor Positioning

TL;DR

BiCSI introduces a novel binary encoding and fingerprint-based algorithm for Wi-Fi indoor positioning, achieving high accuracy and low error by efficiently interpreting CSI data and reducing storage needs.

Contribution

The paper presents BiCSI, a new method combining binary encoding and fingerprinting for improved indoor positioning using Wi-Fi CSI data, outperforming existing approaches.

Findings

Achieves over 98% accuracy in indoor positioning

Reduces data storage to kilobytes, much lower than traditional methods

Maintains robustness across different time periods

Abstract

Traditional global positioning systems often underperform indoors, whereas Wi-Fi has become an effective medium for various radio sensing services. Specifically, utilizing channel state information (CSI) from Wi-Fi networks provides a non-contact method for precise indoor positioning; yet, accurately interpreting the complex CSI matrix to develop a reliable strategy for physical similarity measurement remains challenging. This paper presents BiCSI, which merges binary encoding with fingerprint-based techniques to improve position matching for detecting semi-stationary targets. Inspired by gene sequencing processes, BiCSI initially converts CSI matrices into binary sequences and employs Hamming distances to evaluate signal similarity. The results show that BiCSI achieves an average accuracy above 98% and a mean absolute error (MAE) of less than three centimeters, outperforming algorithms directly dependent on physical measurements by at least two-fold. Moreover, the proposed method for extracting feature vectors from CSI matrices as fingerprints significantly reduces data storage requirements to the kilobyte range, far below the megabytes typically required by conventional machine learning models. Additionally, the results demonstrate that the proposed algorithm adapts well to multiple physical similarity metrics, and remains robust over different time periods, enhancing its utility and versatility in various scenarios.