High-Precision UWB TDOA Localization Algorithm Based on UKF-FNN-CHAN-RIC

Abstract

Ultrawideband (UWB) technology has garnered significant attention due to its extensive applications in indoor positioning. However, its performance is susceptible to interference from environmental factors. In response to the positioning accuracy issues in time difference of arrival (TDOA) scenarios, this article proposes an integrated TDOA-UKF-FNN-Chan-RIC algorithm that incorporates the unscented Kalman filter (UKF), feedforward neural networks (FNNs), Chan’s algorithm, and redundant information correction (RIC) to effectively enhance the positioning accuracy and robustness of UWB systems. Initially, by investigating the redundant information within the UWB solution model in TDOA scenarios, the inter-relationship between redundant information and measurement vectors is revealed. Subsequently, a gradient descent error correction algorithm for redundant information suitable for TDOA scenarios is proposed, and UKF and FNN transformations are combined to ensure that the data meet the preconditions for algorithmic application. To address the stability issues of positioning algorithms based on implicit function solutions in TDOA scenarios, Chan’s algorithm is employed to convert range difference measurements into range measurements, thereby enhancing computational robustness. Ultimately, the TDOA-UKF-FNN-Chan-RIC algorithm is proposed, and its effectiveness is validated through simulation and practical experiments. The experimental results demonstrate that this algorithm significantly improves positioning accuracy and robustness, achieving millimeter-level fixed-point accuracy and centimeter-level track accuracy in practical experiments.