Rosa Zanatta, Mateus daMateus daRosa ZanattaCosta, Joao Paulo C.L. daJoao Paulo C.L. daCostaAntreich, FelixFelixAntreichHaardt, MartinMartinHaardtElger, GordonGordonElgerMendonca, Fabio Lucio Lopes deFabio Lucio Lopes deMendoncaSousa, Rafael Timoteo deRafael Timoteo deSousa2022-03-062022-03-062020https://publica.fraunhofer.de/handle/publica/26631010.1109/ACCESS.2020.3024597Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe).enGlobal Navigation Satellite System (GNSS)Global Positioning System (GPS)GPS3time-delay estimation (TDE)multipath componentmodel order selection (MOS)629004journal article