Improved Soft Rough Set Covering Model for Wireless Indoor Localization: Modeling and Accuracy Analysis

Abstract

Indoor localization based on Received Signal Strength (RSS) fingerprinting remains a prominent paradigm owing to its economic viability and seamless integration with existing wireless infrastructure. Nevertheless, positioning accuracy is frequently compromised by spatiotemporal environmental dynamics, device heterogeneity, and high-variance noise fluctuations inherent in individual Reference Points (RPs). To overcome these limitations, this paper introduces an Improved Soft Rough set-based Covering (I-SRC) model underpinned by a rigorous three-stage localization architecture. Stage (i): Raw offline RSS measurements undergo advanced filtering and structural optimization to construct a robust, noise-resilient radio map. Stage (ii): A specialized SRC methodology is deployed to classify the training instances, effectively mitigating the high dimensionality of the RSS feature space while preserving critical spatial characteristics. Stage (iii): A high-fidelity online matching algorithm correlates real-time RSS vectors with the established offline database to estimate coordinates. Comprehensive evaluations across multiple benchmark datasets demonstrate that the proposed I-SRC framework achieves a robust classification accuracy of approximately 96.38% and 97.75% on the UJI-V.1 and UJI-V.2 datasets, respectively. Crucially, the model yields outstanding positioning precision, recording low Average Positioning Errors (APE) of 0.58 m and 0.64 m on the respective datasets, thereby significantly outperforming contemporary state-of-the-art fingerprinting baselines. These results confirm that the I-SRC framework offers an efficient, scalable, and highly accurate solution for complex indoor positioning environments.