TY - JOUR A1 - Da Rosa Zanatta, Mateus A1 - Carvalho Lustosa da Costa, Joao Paulo A1 - Antreich, Felix A1 - Haardt, Martin A1 - Elger, Gordon A1 - Lopes de Mendonça, Fábio Lúcio A1 - de Sousa Junior, Rafael Timóteo T1 - Tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios BT - IEEE Access N2 - Global 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). KW - Global navigation satellite systems (GNSS) KW - global positioning system (GPS) KW - GPS3 KW - time-delay estimation (TDE) KW - multipath components KW - model order selection (MOS) Y1 - 2020 UR - https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/952 UR - https://doi.org/10.1109/ACCESS.2020.3024597 UR - https://nbn-resolving.org/urn:nbn:de:bvb:573-9528 SN - 2169-3536 VL - 8 SP - 174931 EP - 174942 PB - IEEE CY - Piscataway ER -