Joint Delay and Angle Estimation for GNSS Multipath Signals Based on Spatial and Frequential Smoothing

Chang, Ning; Hong, Xi; Wang, Wenjie; Wang, Zhaonian

doi:10.1109/icsp.2018.8652351

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…To compute the model order applying the Modified Exponential Fitting Test (M-EFT) [24], we use the covariance matrixR[k] obtained from each epoch of the tensor Y (d) as defined in (9). Therefore, we firstly compute the eigenvalue decomposition (EVD) to obtain…”

Section: A Model Order Selection For Dynamic Environmentsmentioning

confidence: 99%

“…Next, let us consider the first-mode unfolding of Y (d) and use the representation in (9) and 14, then, we obtain…”

Section: Estimation Of Factor Matricesmentioning

confidence: 99%

“…Thus, multi-antenna GNSS receivers became the focus of research on resilient positioning withstanding not only multipath but also 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. A tensor-based decomposition features uniqueness, improves the identifiability of the parameters, and the tensor structure permits efficient denoising of the received signal.…”

Section: Introductionmentioning

confidence: 99%

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Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios

et al. 2020

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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 timedelay 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). INDEX TERMS Global Navigation Satellite Systems (GNSS), Global Positioning System (GPS), GPS3, Time-delay estimation (TDE), Multipath components, Model order selection (MOS).

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Section: A Model Order Selection For Dynamic Environmentsmentioning

confidence: 99%

“…Next, let us consider the first-mode unfolding of Y (d) and use the representation in (9) and 14, then, we obtain…”

Section: Estimation Of Factor Matricesmentioning

confidence: 99%