A Novel Intersection-Statistics-Based Indoor TOA Localization Algorithm with Adaptive Error Correction for NLOS Environments

To address the performance degradation of existing base station-based indoor localization algorithms in non-line-of-sight (NLOS) environments, we propose a novel intersection-statistics-based localization method. The proposed algorithm introduces an adaptive error-correction mechanism that mitigates the aggregated effects of multipath interference and environmentally induced variations in TOA measurements. The core innovation lies in establishing a statistical framework that utilizes intersection density within minimum bounding circles to optimize correction parameters. Subsequent refinement employs standard deviation analysis to eliminate spatial outliers before final coordinate estimation. Comparative experimental results demonstrate significant improvements over conventional least squares (LS) and Nano algorithms across three key metrics: mean positioning error (reduced by 38.7%), maximum error (decreased by 42.1%), and error variance (improved by 57.3%). Empirical validation shows that the algorithm achieves 97.36% of absolute positioning errors within 1 m precision under optimized parameters, while maintaining 85.82% sub-meter accuracy using universal correction factors. These performance characteristics satisfy rigorous requirements for commercial indoor positioning systems while providing practical implementation advantages through adaptive parameter tuning.