Minimizing Indoor Localization Errors for Non-Line-of-Sight Propagation

Indoor Localization becomes more important, as it provides additional context for many applications for example in the Internet of Things (IoT), Time-of-flight measurements, as a basis for distance estimation, are susceptible for non-line-of-sight (NLOS) propagation, resulting in large distance errors. Standard least squares solutions to estimate the targets location do not account for NLOS propagation which results in large scale errors. We investigate the difference between L1- and L2-minimization and present a new framework based on a modified RANSAC approach. Additionally, we investigate a Support Vector Machine (SVM) to detect NLOS measurements. We present simulation and measurement results and evaluate our approach. We show that our framework delivers better performance in presence of NLOS propagation compared to plain Ll-or L2-minimization.