In-Motion Alignment Method of SINS Based on Improved Kalman Filter under Geographic Latitude Uncertainty

Sun, Jin; Ye, Qianqi; Yue, Lei

doi:10.3390/rs14112581

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Because of time-varying measurement noise and outliers, the direct point modification from X − i,k|k to X + i,k|k cannot be formulated as (18), i.e., the K k approximates, 0 and X − i,k|k , should be replaced by X i,k|k−1 in cases where there is a GNSS outage. Further,…”

Section: Observability-constrained Resampling-free Ckfmentioning

confidence: 99%

“…In the presence of time-varying noise statistics, model mismatches, and measurement outliers, the SSM of a GNSS/INS appears to have non-Gaussian and nonlinear properties [14][15][16]. Because there are no closed-form solutions for nonlinear and non-Gaussian filtering problems, the suboptimal filter design for navigation and target tracking is an active area of research [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

Cui,

Chen,

Weng

et al. 2023

Remote Sensing

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Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement outliers. It has been proven that the sigma points-based Kalman filter (KF) performs better than an extended KF in cases where large prior uncertainty is present in the state estimation of a GNSS/INS. By modifying the sigma points directly, the resampling-free sigma point update framework (SUF) propagates more information excepting Gaussian moments of prescribed accuracy, based on which the resampling-free cubature Kalman filter (RCKF) was developed in our previous work. In order to enhance the adaptivity and robustness of the RCKF, the resampling-free SUF depending on dynamic prediction residue should be improved by suppressing the time-varying measurement outlier. In this paper, a robust observability-constrained RCKF (ROCRCKF) is proposed based on adaptive measurement noise covariance estimation and outlier detection, where the occurrence of measurement outliers is modelled by the Bernoulli variable and estimated with the state simultaneously. Experiments based on car-mounted GNSS/INSs are performed to verify the effectiveness of the ROCRCKF, and result indicates that the proposed algorithm outperforms the RCKF in the presence of measurement outliers, where the heading error and average root mean square error of the position are reduced from 1.96° and 6.38 m to 0.27° and 5.95 m, respectively. The ROCRCKF is robust against the measurement outliers and time-varying model uncertainty, making it suitable for the real-time implementation of GNSS/INSs in GNSS-challenged environments.

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Section: Observability-constrained Resampling-free Ckfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

Cui,

Chen,

Weng

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Moreover, most of the existing high-precision SINS products also use rotary modulation to improve navigation accuracy. Thus far, many scholars have conducted much useful research work on initial alignment [16][17][18], error modulation [19], rotation modulation schemes [20,21], solution algorithms [22,23], etc., and achieved rich research results. However, as one of the important application scenarios of SINS, there is little research on the rotation error modulation and field applicability of shearers.…”

Section: Introductionmentioning

confidence: 99%

Simulation Optimization and Application of Shearer Strapdown Inertial Navigation System Modulation Scheme

Fang

Yang

et al. 2023

Sensors

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The operating attitude of a shearer based on a three-dimensional (3D) space scale is the necessary basic information for realizing intelligent mining. Aiming to address the problem of the insufficient perception accuracy of shearers, in this paper, the rotation model of the actual turning mechanism of the strapdown inertial navigation system (SINS) of shearers is established, and the error propagation characteristics of different single-axis rotation modulation schemes are revealed. Through theory and simulation, the optimal rotation modulation scheme is determined to be the improved four-position turn–stop modulation with a rotation of <360°. The experiment shows that the 24 h positioning error of this scheme is 3.7 nmile, and the heading angle changes by 0.06°, which proves that this scheme can effectively improve the attitude perception accuracy of the inertial navigation system (INS). The field application of the shearer operating attitude perception based on this scheme shows that the positioning error after error compensation is 17% of that before compensation, and the heading angle error is 75% of that before compensation, which verifies that this scheme can significantly improve the accuracy of shearer operating attitude perception in field applications. This scheme can achieve higher precision perception accuracy based on SINS and has broad application prospects in the field of high-precision pose perception of coal mining machines, roadheaders, and other equipment.

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3D LiDAR Aided GNSS/INS Integration Fault Detection, Localization and Integrity Assessment in Urban Canyons

Wang

Dan

et al. 2022

Remote Sensing

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The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection and localization algorithms are needed to ensure integrity. Currently, the residual chi-square test is used for fault detection in the positioning domain, but it has poor sensitivity when faults disappear. Three-dimensional (3D) light detection and ranging (LiDAR) has good positioning performance in complex environments. First, a LiDAR aided real-time fault detection algorithm is proposed. A test statistic is constructed by the mean deviation of the matched targets, and a dynamic threshold is constructed by a sliding window. Second, to solve the problem that measurement noise is estimated by prior modeling with a certain error, a LiDAR aided real-time measurement noise estimation based on adaptive filter localization algorithm is proposed according to the position deviations of matched targets. Finally, the integrity of the integrated system is assessed. The error bound of integrated positioning is innovatively verified with real test data. We conduct two experiments with a vehicle going through a viaduct and a floor hole, which, represent mid and deep urban canyons, respectively. The experimental results show that in terms of fault detection, the fault could be detected in mid urban canyons and the response time of fault disappearance is reduced by 70.24% in deep urban canyons. Thus, the poor sensitivity of the residual chi-square test for fault disappearance is improved. In terms of localization, the proposed algorithm is compared with the optimal fading factor adaptive filter (OFFAF) and the extended Kalman filter (EKF). The proposed algorithm is the most effective, and the Root Mean Square Error (RMSE) in the east and north is reduced by 12.98% and 35.1% in deep urban canyons. Regarding integrity assessment, the error bound can overbound the positioning errors in deep urban canyons relative to the EKF and the mean value of the error bounds is reduced.

show abstract

In-Motion Alignment Method of SINS Based on Improved Kalman Filter under Geographic Latitude Uncertainty

Cited by 3 publications

References 27 publications

Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

Simulation Optimization and Application of Shearer Strapdown Inertial Navigation System Modulation Scheme

3D LiDAR Aided GNSS/INS Integration Fault Detection, Localization and Integrity Assessment in Urban Canyons

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