GNSS/INS Tightly Coupled Navigation with Robust Adaptive Extended Kalman Filter

Wu, Youlong; Chen, Shuai; Yin, Tingting

doi:10.1007/s12239-022-0142-7

Cited by 13 publications

(9 citation statements)

References 35 publications

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“…The traditional KF method and its extensions, such as extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman Filter (CKF), are the most common integration algorithms. The effectiveness and robustness of KF-based algorithm have been verified by several studies [14][15][16][17][18][19][20][21]. Kim et al [17] employed EKF to integrate linearized correlator outputs and INS measurements.…”

Section: Introductionmentioning

confidence: 96%

“…All sensor measurements are considered as related state variables (i.e., factors) of the factor graph, which are used to construct the function of maximization problems. This technique is widely used in a variety of fields such as simultaneous localization and mapping (SLAM), artificial intelligence, and wireless networking [15,27]. Studies have also shown that FGO can remarkably enhance the positioning performance in GNSS/INS integration compared with the KF-based methods [28].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

Li,

Lee,

Hung

et al. 2023

Remote Sensing

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Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration between GNSS and inertial navigation systems (INSs) could be utilized to improve positioning performance. However, the fixed GNSS solution uncertainty of the conventional integration method cannot determine the fluctuating GNSS reliability in fast-changing urban environments. This weakness becomes solvable using a deep learning model for sensing the ambient environment intelligently, and it can be further mitigated using factor graph optimization (FGO), which is capable of generating robust solutions based on historical data. This paper mainly develops the adaptive GNSS/INS loosely coupled system on FGO, along with the fixed-gain Kalman filter (KF) and adaptive KF (AKF) being taken as comparisons. The adaptation is aided by a convolutional neural network (CNN), and the feasibility is verified using data from different grades of receivers. Compared with the integration using fixed-gain KF, the proposed adaptive FGO (AFGO) maintains the 100% positioning availability and reduces the overall 2D positioning error by up to 70% in the aspects of both root mean square error (RMSE) and standard deviation (STD).

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

Li,

Lee,

Hung

et al. 2023

Remote Sensing

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“…However, it can only handle colored process noise and cannot address colored measurement noise [36], [37]. A robust adaptive filter can effectively counteract the effects of colored process noise and colored measurement noise, but it requires redundant measurements [38]. Although the above-mentioned filters have been demonstrated to be effective in mitigating the impact of colored noise, they all employ a Gaussian distribution with increased variance to encompass the true non-Gaussian distribution when dealing with non-Gaussian noise problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Gaussian Sum Extended Kalman Filter With Colored Noise for GNSS/SINS Tightly Coupled Positioning and Attitude Determination Systems

Dai,

Xiao,

Wan

et al. 2024

IEEE Access

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The Gaussian sum extended Kalman filter (GSEKF), as a nonlinear non-Gaussian filter, disregards the impact of colored noise components in non-Gaussian noise engendered by external interference, which may compromise the estimation accuracy in global navigation satellite system/strapdown inertial navigation system (GNSS/SINS) tightly coupled positioning and attitude determination systems. To address this problem, an improved Gaussian sum extended Kalman filter with colored noise (colored-GSEKF) is proposed to refine the random model by approximating non-Gaussian noise using Gaussian mixture models and whitening the colored noise components within the non-Gaussian colored noise through state and measurement augmentation. This improved algorithm further enhances the accuracy and stability of the filtering estimation in non-Gaussian colored noise environments. Simulations and experimental results indicate that the proposed colored-GSEKF exhibits superior accuracy in modeling a random model in non-Gaussian colored noise environments. By applying this filter to GNSS/SINS tightly coupled positioning and attitude determination systems utilized on unmanned aerial vehicles influenced by non-Gaussian colored noise, the estimation accuracy and stability can be improved. INDEX TERMSColored noise, extended Kalman filter, Gaussian sum filter, GNSS/SINS tightly coupled positioning and attitude determination systems, non-Gaussian noise, unmanned aerial vehicles.

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“…The effectiveness of EKF depends largely on whether the mathematical model developed is consistent with the actual situation [13]. With the precise mathematical model of integrated navigation and the statistical characteristics of system noise, GNSS/INS integration based on EKF combines the advantages of two systems to provide superior performance over a single system [14,15].…”

Section: Introductionmentioning

confidence: 99%

The accuracy of vehicle position and heading angle improvement based on dual-antenna GNSS/INS/Barometer integration using extended Kalman filter

Jiao,

Xu,

Chen

et al. 2024

Meas. Sci. Technol.

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In the field of autonomous driving progresses, obtaining the precise position and attitude of vehicles through measurement technology is vital. An integrated GNSS/INS/Barometer (Global Navigation Satellite System/Inertial Navigation System/Barometer) fusion method is proposed to improve vehicle positioning and heading angle accuracy. An extended Kalman filter (EKF) based on horizontal dilution of precision (HDOP) is designed to fuse the INS error and the GNSS measurement. Furthermore, GNSS dual antennas are utilized to increase a state variable in EKF based on HDOP in addition to position and velocity. The kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to measure the mounting angle to reduce error. The position and heading angle accuracy of the proposed method are 0.483m and 1.358°, which represent reductions of 37.03% and 43.86% compared to the conventional EKF method, respectively. The experimental results demonstrate that the proposed method can measure accurate position and heading angle in both open and occluded environments.

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GNSS/INS Tightly Coupled Navigation with Robust Adaptive Extended Kalman Filter

Cited by 13 publications

References 35 publications

Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

An Improved Gaussian Sum Extended Kalman Filter With Colored Noise for GNSS/SINS Tightly Coupled Positioning and Attitude Determination Systems

The accuracy of vehicle position and heading angle improvement based on dual-antenna GNSS/INS/Barometer integration using extended Kalman filter

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