UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning
Hongchao Yang, Yunjia Wang, Chee Kiat Seow, Meng Sun, Minghao Si, Lu Huang
Abstract
Ultrawideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-LOS (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCPs) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This article derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed support vector machine (SVM)-based classification methodology leverages the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27%, and the LOS and NLOS recalls are 94.27% and 92.57%, respectively. The ranging errors in LOS (NLOS) conditions are reduced from 0.106 (1.442 m) to 0.065 (0.739 m), demonstrating an improvement of 38.85% (48.74%) with 0.041 (0.703 m) error reduction. In contrast, the average positioning accuracy is also reduced from 0.250 to 0.091 m with an improvement of 63.49% (0.159 m), which outperforms the state-of-the-art approaches of the least-squares SVM (LS-SVM) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -nearest neighbor (KNN) algorithms.