ToA and TDoA Estimation Using Artificial Neural Networks for High-Accuracy Ranging - Kirmaz, Anil; Sahin, Taylan; Michalopoulos, Diomidis S.; Gerstacker, Wolfgang (IEEE Journal on Selected Areas in Communications, 2023)
High-accuracy positioning enables various applications such as industrial asset tracking, autonomous driving and process automation. Accurate location information relies on accurate time-of-arrival (ToA) or time-difference-of-arrival (TDoA) estimation in widely utilized time-based ranging. In this paper, we propose artificial neural network (ANN) based methods either to estimate T(D)oA directly or to mitigate the error of the conventional estimators. Based on real-world channel measurements, we show that the proposed direct ANN estimator outperforms the conventional estimators at least by approximately 37% and 24% in the 90th percentile ranging error, derived from ToA and TDoA estimations, respectively. Additionally, the proposed T(D)oA error-mitigating ANNs outperform the benchmark error mitigation methods with a gain varying between 17-43% in the 90th percentile ranging error, depending on the underlying conventional estimator. The ranging accuracy delivered by the direct estimation and error mitigation methods using ANNs are similar. Furthermore, the ANN estimators yield a more robust performance than the conventional estimators when the carrier frequency of the positioning signal is varied. ANN-based ToA estimation yields a marginally better ranging accuracy than ANN-based TDoA estimation. This advantage comes at the expense of a larger communication latency, while avoiding the need for synchronization among the positioning anchors.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Artificial neural networks; Artificial neural networks; Distance measurement; Estimation; Fingerprint recognition; indoor positioning systems; Measurement uncertainty; Position measurement; ranging; Receivers; time difference of arrival; time of arrival estimation