Abstract
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).
Highlights
As Global Navigation Satellite Systems (GNSS) become more ubiquitous and this technology proved to be essentialThe associate editor coordinating the review of this manuscript and approving it for publication was Wei Wang .for applications such as civilian aviation, autonomous driving, defense, and timing and synchronization of critical networks
We proposed a tensor-based framework capable of performing model order estimation and factor decomposition for time-delay estimation in dynamic multipath scenarios
To perform high accuracy factor decomposition, we exploit the model order estimates for each slice to group the slices into sub-tensors
Summary
As Global Navigation Satellite Systems (GNSS) become more ubiquitous and this technology proved to be essentialThe associate editor coordinating the review of this manuscript and approving it for publication was Wei Wang .for applications such as civilian aviation, autonomous driving, defense, and timing and synchronization of critical networks. GNSS receivers require line of sight (LOS) signals from at least four satellites to estimate their position on the Earth’s surface. The superposition of the LOS and NLOS multipath components degrades the time-delay estimation (TDE) and, the positioning estimation. State-of-the-art GNSS receivers equipped with a single antenna are in general remarkably sensitive to the effect of multipath components [1]–[3]. Multi-antenna GNSS receivers became the focus of research on resilient positioning withstanding multipath and interference and spoofing. In addition to beamforming approaches [4], [5], multi-dimensional parameter estimation approaches [6], [7], and other approaches as those proposed in [8] and [9], tensor-based decomposition methods showed significant improvements over matrix-based decomposition methods. Tensor-based multipath mitigation methods, combined with antenna arrays, have been proposed as an alternative to single antenna and matrix-based techniques
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