Precise instantaneous velocimetry and accelerometry with a stand-alone GNSS receiver based on sparse kernel learning

Chang, Guobin; Qian, Nijia; Chen, Chao; Gao, Jingxiang

doi:10.1016/j.measurement.2020.107803

Cited by 19 publications

(8 citation statements)

References 40 publications

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“…In this work, a highly efficient method, called FISTA, is employed [26]. This algorithm has also been used in our previous studies [28][29][30]. It is a proximal gradient algorithm, which is further accelerated by the Nesterov momentum method [31].…”

Section: Computation Algorithmmentioning

confidence: 99%

Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products

et al. 2022

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High-frequency and correlated noise filtering is one of the important preprocessing steps for GRACE level-2 products before calculating mass anomaly. Decorrelation and denoising kernel (DDK) filters are usually considered as such optimal filters to solve this problem. In this work, a sparse DDK filter is proposed. This is achieved by replacing Tikhonov regularization in traditional DDK filters with weighted L1 norm regularization. The proposed sparse DDK filter adopts a time-varying error covariance matrix, while the equivalent signal covariance matrix is adaptively determined by the Gravity Recovery and Climate Experiment (GRACE) monthly solution. The covariance matrix of the sparse DDK filtered solution is also developed from the Bayesian and error-propagation perspectives, respectively. Furthermore, we also compare and discuss the properties of different filters. The proposed sparse DDK has all the advantages of traditional filters, such as time-varying, location inhomogeneity, and anisotropy, etc. In addition, the filtered solution is sparse; that is, some high-degree and high-order terms are strictly zeros. This sparsity is beneficial in the following sense: high-degree and high-order sparsity mean that the dominating noise in high-degree and high-order terms is completely suppressed, at a slight cost that the tiny signals of these terms are also discarded. The Center for Space Research (CSR) GRACE monthly solutions and their error covariance matrices, from January 2004 to December 2010, are used to test the performance of the proposed sparse DDK filter. The results show that the sparse DDK can effectively decorrelate and denoise these data.

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Section: Computation Algorithmmentioning

confidence: 99%

Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products

et al. 2022

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“…Meanwhile, the train positioning, airborne gravimetry, automatic driving and aircraft docking also need highaccuracy velocity information. Traditionally, there are three methods to determine velocity, namely position derivation (PD), Doppler observations (DO) and Time-difference carrier phase (TDCP), respectively [10,11]. According to the related results, it is well known that the precision of TDCP method is optimal, while the DO method can provide a more reliable velocity during the sudden changes of vehicle status [12,13].…”

Section: Introductionmentioning

confidence: 99%

Improved Velocity Determination Method by the Integration of Multi-GNSS Satellites’ Doppler and Phase Observations

Zhang

2024

IEEE Access

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Velocity determination as one of the widely used spatial information is essential for the location-based services. Concerned researches about Global Navigation Satellite System (GNSS) velocity determination mainly focus on carrier phase observations, which ignored the merits of multi-type observations transmitted by multi-GNSS satellites. To increase the performances of GNSS velocity determination, the Helmert variance components estimation (VCE) algorithm is introduced into the integration of Doppler and carrier phase observations, moreover a velocity vector constraint is designed with the aid of original phase observations, constructing the condition equation, and inserting into GNSS velocity determination model. According to kinematics experiments, results show that the integrated Doppler and carrier phase observations can obtain accuracies of cm/s level for E, N and U directions, with improvements of 94.1%, 93.9% and 89.5%, respectively, compared with Doppler-only scheme. However, the obvious accuracy reductions are occurred during the satellite signal obstruction, where the corresponding improvements with 89.7%, 90.1% and 69.4% for E, N and U directions can be obtained. Meanwhile, the constraint condition can ameliorate the impacts of signal obstruction, improvements with 30.8%, 8.3% and 5.0% for E, N and U directions based on the integration solution.

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“…This prior knowledge can be coded in a variable called the signal covariance matrix (SCM). By combining both the ECM and the SCM through a regularization/inversion framework, we can reduce both kinds of errors [9,10]. This methodology, often called DDK filtering, has clear statistical meanings [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive DDK Filter for GRACE Time-Variable Gravity Field with a Novel Anisotropic Filtering Strength Metric

et al. 2022

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Filtering for GRACE temporal gravity fields is a necessary step before calculating surface mass anomalies. In this study, we propose a new denoising and decorrelation kernel (DDK) filtering scheme called adaptive DDK filter. The involved error covariance matrix (ECM) adopts nothing but the monthly time-variable released by several data centers. The signal covariance matrix (SCM) involved is monthly time-variable also. Specifically, it is parameterized into two parameters, namely the regularization coefficient and the power index of signal covariances, which are adaptively determined from the data themselves according to the generalized cross validation (GCV) criterion. The regularization coefficient controls the global constraint on the signal variances of all degrees, while the power index adjusts the attenuation of the signal variances from low to high degrees, namely local constraint. By tuning these two parameters for the monthly SCM, the adaptability to the data and the optimality of filtering strength can be expected. In addition, we also devise a half-weight polygon area (HWPA) of the filter kernel to measure the filtering strength of the anisotropic filter more reasonably. The proposed adaptive DDK filter and filtering strength metric are tested based on CSR GRACE temporal gravity solutions with their ECMs from January 2004 to December 2010. Results show that the selected optimal power indices range from 3.5 to 6.9, with the corresponding regularization parameters range from 1 × 1014 to 5 × 1019. The adaptive DDK filter can retain comparable/more signal amplitude and suppress more high-degree noise than the conventional DDK filters. Compared with the equivalent smoothing radius (ESR) of filtering strength, the HWPA has stronger a distinguishing ability, especially when the filtering strength is similar.

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Precise instantaneous velocimetry and accelerometry with a stand-alone GNSS receiver based on sparse kernel learning

Cited by 19 publications

References 40 publications

Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products

Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products

Improved Velocity Determination Method by the Integration of Multi-GNSS Satellites’ Doppler and Phase Observations

Adaptive DDK Filter for GRACE Time-Variable Gravity Field with a Novel Anisotropic Filtering Strength Metric

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