Enhanced WiFi RTT Localization via Wavelet Transform-Based LoS/NLoS Identification and Ranging Denoising

Smartphone-based Wi-Fi round-trip time (RTT) indoor localization is significantly degraded by line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. To address this, we propose an enhanced RTT localization framework that leverages wavelet transform for joint LoS/NLoS identification and ranging denoising. Specifically, continuous wavelet transform (CWT) first extracts normalized coefficients from raw RTT data, which are then fed into a Gaussian process classifier (GPC) to identify LoS/NLoS states. Based on these classification results, NLoS-induced ranging errors are subsequently mitigated via discrete wavelet transform-based denoising with an adaptive threshold strategy. Experiments demonstrate that in the office scenario, the proposed CWT-GPC identifier reduces the fail rate by 10.10, 21.50, and 15.50 percentage points compared with SVM, RF, and GPC, respectively; in the corridor scenario, the corresponding reductions are 8.26, 27.84, and 26.95 percentage points. In the office scenario, the denoising step reduces the mean localization error by 0.584 m, 0.406 m, and 0.232 m compared with LC, NLC, and GPRC, respectively. In the corridor scenario, the corresponding reductions are 1.252 m, 0.743 m, and 0.561 m, respectively. These results validate significant gains in both identification robustness and positioning precision.