Integration of variance component estimation with robust Kalman filter for single-frequency multi-GNSS positioning

Bahadur, Berkay; Nohutçu, Metin

doi:10.1016/j.measurement.2020.108596

Cited by 15 publications

(5 citation statements)

References 48 publications

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“…Alternatively, Guo and Zhang (2014) followed an iterative procedure applying the IGG III function for the observation with the largest standardized residual in each iteration. The main reason for the use of this iterative procedure is the avoidance of filter divergence and a reduction in the contribution of the normal observations (Bahadur and Nohutcu 2021). Therefore, this iterative procedure is adopted in this study to conserve the original distributional properties of the observations.…”

Section: Variance Modelsmentioning

confidence: 99%

Improving the stochastic model for code pseudorange observations from Android smartphones

Bahadur,

Schön

2024

GPS Solut

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In recent years, there has been increasing attention to positioning, navigation, and timing applications with smartphones. Because of frequently disrupted carrier phase observations, code observations remain critical for smartphone-based positioning. Considering a realistic stochastic model is mandatory to obtain the utmost positioning performance, this study proposes a sound stochastic approach for code observations from Android smartphones. The proposed approach includes a modified version of the SIGMA-ɛ variance model with different coefficients for each GNSS constellation and a robust Kalman filter method. First the noise characteristics of observations from the Xiaomi Mi 8 smartphone are analyzed utilizing code-minus-phase combinations to estimate the coefficients for each GNSS constellation. This includes the determination of a variance model as well as a check of the probability distribution. Finally, the proposed approach is validated in the positioning domain using single-frequency code observation-based real-time standalone positioning. The results show that more than 95% of observations follow the normal distribution when the proposed approach is applied. Compared with the conventional stochastic approach, including a C/N0-dependent model and standard Kalman filter, it improves the positioning accuracy by 45.8% in a static experiment, while its improvement is equal to 26.6% in a kinematic experiment. For the static and kinematic experiments, in 50% of the epochs, the 3D positioning errors are smaller than 3.0 m and 3.4 m for the proposed stochastic approach. The results exhibit that the stochastic properties of code observations from smartphones can be successfully represented by the proposed approach.

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Section: Variance Modelsmentioning

confidence: 99%

Improving the stochastic model for code pseudorange observations from Android smartphones

Bahadur,

Schön

2024

GPS Solut

Self Cite

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“…2 ), which indicates the differential code biases (DCBs) between the related frequencies (Bahadur and Nohutcu, 2021b). In the single-frequency positioning, the timing group delay is must be corrected for aligning the satellite clock correction.…”

Section: Single-frequency Code-based Positioningmentioning

confidence: 99%

Real-time single-frequency precise positioning with Galileo satellites

Bahadur

2021

J. Navigation

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Following substantial progress achieved recently, the Galileo constellation provides a considerable satellite resource for the GNSS applications. In this regard, the performance assessment of real-time single-frequency precise positioning with Galileo satellites is the main objective of this research. For this purpose, several experimental tests were conducted in this study with two single-frequency positioning models, namely single-frequency code-based positioning and code-phase combination. The results show that Galileo presents an adequate number of visible satellites sufficient for single-frequency positioning. Also, the study demonstrates that, in comparison to GPS observations, Galileo observations have a significantly lower noise level. For the single-frequency code-based positioning, Galileo presents a better positioning accuracy than GPS by 25⋅8% on average. When compared with GPS, a 9⋅4% better positioning accuracy is acquired from Galileo for the single-frequency code-phase combination, with its average convergence time shorter than GPS by a ratio of 24⋅4%.

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“…In [17], Ahmed introduced a novel DRKF technique that constructs a statistically robust equivalent weight model using the chi-square test, thereby enhancing placement outcomes. To address the GNSS outliers, Berkay et al integrated robust KF with variance component estimation to dynamically set weights and variances for multi-GNSS observations in single-frequency positioning [18].…”

Section: Introductionmentioning

confidence: 99%

Robust GNSS Positioning Using Unbiased Finite Impulse Response Filter

Dou,

Xu,

Dou

2023

Remote Sensing

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In a typical GNSS receiver, pseudorange and pseudorange rate measurements are generated through the code and carrier tracking loops, respectively. These measurements are then employed to calculate the user’s position and velocity (PV) solutions, which is typically achieved using a Kalman filter (KF) or the least squares (LS) algorithm. However, the LS method only uses the current observation without error analysis. The positioning result is greatly affected by the errors in the observed data. In KF, by using an iterative approach that combines predictions and measurements of PV information, more accurate estimates can be obtained because the PV information is time-correlated. Meanwhile, its optimal estimate requires that both the model and noise statistics are exactly known. Otherwise, achieving optimality cannot be guaranteed. To address this issue, this paper proposes and implements a novel GNSS solution method based on an unbiased finite impulse response (UFIR) filter. Two different field tests were conducted. The position results of UFIR are compared with those from the LS and KF methods, and the horizon positioning mean error is improved by 44% and 29%, respectively, which highlights its efficacy. The method offers two primary benefits: it is robust to noise uncertainty, and it leverages historical data within the UFIR framework to provide a more accurate estimate of the current state.

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Integration of variance component estimation with robust Kalman filter for single-frequency multi-GNSS positioning

Cited by 15 publications

References 48 publications

Improving the stochastic model for code pseudorange observations from Android smartphones

Improving the stochastic model for code pseudorange observations from Android smartphones

Real-time single-frequency precise positioning with Galileo satellites

Robust GNSS Positioning Using Unbiased Finite Impulse Response Filter

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