Constrained MEMS-Based GNSS/INS Tightly Coupled System With Robust Kalman Filter for Accurate Land Vehicular Navigation

Wang, Dingjie; Dong, Yi; Li, Zhaoyang; Li, Qingsong; Wu, Jie

doi:10.1109/tim.2019.2955798

Cited by 76 publications

(29 citation statements)

References 40 publications

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“…However, these technologies are dependent on geographic information data or external sensor equipment, such as 3D building models, cameras, and LiDAR, which have a certain level of deficiencies in terms of availability, cost, and security. In addition, the equivalent weight model is employed to construct robust algorithms that can weaken the influence of the gross error of the observation on the positioning accuracy [ 36 , 37 ]. This method utilizes the robust factor to adjust the filter gain moment or the observation noise for GNSS/INS integrated positioning, which plays a role in suppressing multipath and NLOS errors to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

et al. 2022

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Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the K-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the K-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.

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Section: Introductionmentioning

confidence: 99%

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

et al. 2022

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“…Ultra-tight integration, on the other hand, requires access to the internal GNSS receiver hardware, which may not be possible for end-users. Tightly coupled (TC) GNSS PPP/MEMS-based INS integration has been addressed by many researchers for land vehicular navigation [10,[12][13][14]. In [10], TC GPS PPP/INS integration algorithms were developed using a Trimble R10 geodetic receiver and a NovAtel CPT-IMU.…”

Section: Introductionmentioning

confidence: 99%

“…Different estimation filters were used by many studies, including the robust Kalman filter RKF [14], unscented Kalman filter (UKF) [16], unscented particle filter (UPF) [17], extended particle filter (EPF) [16], and particle filter (PF) [18]. In [14], a TC GNSS/INS integration algorithm through the RKF was developed using the NovAtel OEM3 geodetic-grade receiver and Stim300 tactical-grade IMU. It was shown that the positioning accuracy was improved significantly through the RKF compared to the conventional EKF.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Low-Cost Tightly Coupled Triple-Constellation GNSS PPP/MEMS-Based INS Integration for Land Vehicular Applications

Elmezayen

El-Rabbany

2021

Geomatics

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The rapid rise of ultra-low-cost dual-frequency GNSS chipsets and micro-electronic-mechanical-system (MEMS) inertial sensors makes it possible to develop low-cost navigation systems, which meet the requirements for many applications, including self-driving cars. This study proposes the use of a dual-frequency u-blox F9P GNSS receiver with xsens MTi670 industrial-grade MEMS IMU to develop an ultra-low-cost tightly coupled (TC) triple-constellation GNSS PPP/INS integrated system for precise land vehicular applications. The performance of the proposed system is assessed through comparison with three different TC GNSS PPP/INS integrated systems. The first system uses the Trimble R9s geodetic-grade receiver with the tactical-grade Stim300 IMU, the second system uses the u-blox F9P receiver with the Stim300 IMU, while the third system uses the Trimble R9s receiver with the xsens MTi670 IMU. An improved robust adaptive Kalman filter is adopted and used in this study due to its ability to reduce the effect of measurement outliers and dynamic model errors on the obtained positioning and attitude accuracy. Real-time precise ephemeris and clock products from the Centre National d’Etudes Spatials (CNES) are used to mitigate the effects of orbital and satellite clock errors. Three land vehicular field trials were carried out to assess the performance of the proposed system under both open-sky and challenging environments. It is shown that the tracking capability of the GNSS receiver is the dominant factor that limits the positioning accuracy, while the IMU grade represents the dominant factor for the attitude accuracy. The proposed TC triple-constellation GNSS PPP/INS integrated system achieves sub-meter-level positioning accuracy in both of the north and up directions, while it achieves meter-level positioning accuracy in the east direction. Sub-meter-level positioning accuracy is achieved when the Stim300 IMU is used with the u-blox F9P GNSS receiver. In contrast, decimeter-level positioning accuracy is consistently achieved through TC GNSS PPP/INS integration when a geodetic-grade GNSS receiver is used, regardless of whether a tactical- or an industrial-grade IMU is used. The root mean square (RMS) errors of the proposed system’s attitude are about 0.878°, 0.804°, and 2.905° for the pitch, roll, and azimuth angles, respectively. The RMS errors of the attitude are significantly improved to reach about 0.034°, 0.038°, and 0.280° for the pitch, roll, and azimuth angles, respectively, when a tactical-grade IMU is used, regardless of whether a geodetic- or low-cost GNSS receiver is used.

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“…Accurate and reliable positioning is of great significance to aircraft and other highspeed carrier navigation. Various kinds of sensors, including IMU, GPS, and SAR, have been developed for positioning [1][2][3][4][5][6][7][8][9][10][11]. Nevertheless, every sensor has its pros and cons and it is therefore not reliable to depend only on one particular sensor for positioning.…”

Section: Introductionmentioning

confidence: 99%

“…GPS is a suitable sensor to be fused with IMU, since it has high absolute positioning accuracy and positioning error will not accumulate with time [4,7]. Wang et al propose a robust tightly coupled GNSS/MEMS-SINS navigation approach aided by nonholonomic constraint, and it is shown that the stand-alone accuracy is improved during 60 s GNSS outages [5]. Li et al proposed a new GPS/INS (inertial navigation system, INS) hybrid method to bridge GPS outages such that reliable and accurate positioning results can be achieved [6].…”

Section: Introductionmentioning

confidence: 99%

A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations

et al. 2021

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A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit (IMU), synthetic aperture radar (SAR), and global positioning system (GPS) are presented. In particular, IMU credibility is modeled by noises and constant drifts that are accumulated with time in a strapdown inertial navigation system (SINS). The quality of the SAR image decides the credibility of positioning based on SAR image matching. In addition, a cumulative residual chi-square test is used to evaluate GPS credibility. An extended Kalman filter based on a sensor credibility evaluation is introduced to integrate the measurements. The measurement of a sensor is either discarded when its credibility value is below a threshold or the variance matrix for the estimated state is otherwise adjusted. Simulations show that the final fusion positioning accuracy with credibility evaluation can be improved by 1–2 times compared to that without evaluation. In addition, the derived standard deviation correctly indicates the value of the position error with credibility evaluation. Moreover, the experiments on an unmanned ground vehicle partially verify the proposed evaluation method of GPS and the fusion framework in the actual environment.

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Constrained MEMS-Based GNSS/INS Tightly Coupled System With Robust Kalman Filter for Accurate Land Vehicular Navigation

Cited by 76 publications

References 40 publications

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

Ultra-Low-Cost Tightly Coupled Triple-Constellation GNSS PPP/MEMS-Based INS Integration for Land Vehicular Applications

A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations

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