Multiepoch Least-Squares Joint Solution for Anchor Self-Localization: Exemplified With UWB

In indoor positioning through the Internet of Things (IoT), the accuracy of anchor locations profoundly impacts receiver localization. At present, the determination of anchor positions primarily relies on traditional manual surveying techniques. The considerable costs of manual surveying have led the academic community to advocate for anchor self-localization methods as viable alternatives. However, existing methodologies prove inadequate in complex indoor environments lacking supplementary information sources, particularly in addressing non-line-of-sight (NLOS) errors that impact anchor positioning accuracy. To address these challenges, this study utilizes Ultra-Wideband (UWB) signal sources as a case study. We mainly propose a novel anchor self-localization method that relies solely on ranging measurements. The method integrates the proposed NLOS bias model based on temporal and spatial correlation to overcome NLOS effects. The method initially employs an initialization algorithm to obtain coarse estimates of anchor positions through multi-epoch least squares (LS) joint estimation, followed by a two-step iterative algorithm that utilizes the expectation maximization principle to optimize these estimates further. The effectiveness of the proposed method is validated through anchor self-localization tests in indoor environments with decimeter-level positioning accuracy results. The root mean square error (RMSE) of the subsequent receiver positioning results was 0.1936m, validating that the attained anchor positions satisfy pragmatic application standards.