BDS PPP/INS Tight Coupling Method Based on Non-Holonomic Constraint and Zero Velocity Update

Sun, Wei; Yang, Yihan

doi:10.1109/access.2020.3008849

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…When carrying out INS/TZV/NHC error modeling, on the basis of the traditional 15-dimensional zero-velocity corrected Kalman filter [26], consider the azimuth installation deflection angle δα ψ and pitch installation deflection angle δα θ between the INS and the vehicle body [27], and the strapdown The installation lever arm error δL H between INS and the equivalent kinematic constraint measurement point O H [28], the state space model can be expanded to 20 dimensions, and the state vector X T H is as follows:…”

Section: Ins/tzv/nhc Filtering Algorithm Design Based On Vehicle Moti...mentioning

confidence: 99%

An Improved Autonomous Inertial-Based Integrated Navigation Scheme Based on Vehicle Motion Recognition

Yu,

Wei,

et al. 2023

IEEE Access

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The autonomous navigation technology of INS has always attracted the attention of many scholars. In satellite-denied environment, making full use of the auxiliary sensors with autonomous characteristics can suppress the dynamic error generated by INS in autonomous navigation effectively. However, auxiliary sensors like an odometer (OD) and laser doppler velocimeter (LDV) will cause instability to the navigation system. To solve the above problems, we propose an improved autonomous inerital-based integrated navigation scheme utilizing vehicle motion recognition. The main research work can be summarized as: (1) Non-holonomic constrained velocity model and tri-axis zero velocity model has been established for inertial-based integrated navigation model. (2) Vehicle motion state recognition method has been proposed utilizing the IMU data. As shown in the experimental results, the proposed method can estimate the IMU biases and increase the positioning accuracy effectively as compared with the traditional methods. INDEX TERMS In-motion alignment, strapdown inertial navigation system (SINS), odometer (OD), backtracking.

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Section: Ins/tzv/nhc Filtering Algorithm Design Based On Vehicle Moti...mentioning

confidence: 99%

An Improved Autonomous Inertial-Based Integrated Navigation Scheme Based on Vehicle Motion Recognition

Yu,

Wei,

et al. 2023

IEEE Access

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“…However, due to the cumulative errors associated with odometers, for the long-duration SINS/OD dead-reckoning system, the navigation accuracy can still diverge. During the map-matching phrase, the ellipsoidal coordinates need to be projected to the plane coordinates using the Gaussian Krüger projection, and then the position outputted from the combined navigation system of SINS/OD is projected to the digital track map to constrain the cross-track error [25]. Afterward, the train's position is calculated using the speed of the odometer and the direction of the digital track map of the track; finally, the two positions are weighted as the train position.…”

Section: Onboard Train Localization Algorithmmentioning

confidence: 99%

A Digital Track Map-Assisted SINS/OD Fusion Algorithm for Onboard Train Localization

Chen,

Yang,

2023

Applied Sciences

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Accurate and reliable speed and position estimation plays an important role in the safety and efficiency of intelligent railway vehicles. Due to the level required of safety, reliability, and strong norms in the current practical application, intelligent railway vehicle positioning heavily relies on a large number of balises laid on the track and the onboard odometer (OD), while the other position method, GNSS introduction, is relatively slow. This article proposed a digital track map-assisted onboard railway location system using strapdown inertial navigation system (SINS) and OD. The proposed method consists of two steps. First, an SINS- and OD-integrated navigation method based on OD velocity integration is in the inner circle. Then, a map-matching algorithm based on vertical projection and heading weighting was employed, and when the matching outer circle results were obtained, the positions obtained from the matching outer circles were used to replace the positions obtained from SINS/OD for the Kalman filter combination. The performance of our algorithm was verified using field tests, and SINS/OD and SINS/OD/MM comparison data processing results prove that our proposed digital track map-assisted SINS/OD algorithm can effectively suppress the accumulation of train position errors. After nearly 80 km of navigation, the position error is 24 m, and the relative mileage accuracy is less than or equal to 0.03% distance.

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“…In motion scenarios, the vehicle does not slip sideways and upward, which means that the velocity on lateral and vertical will be close to zero and only the forward speed exists [31,32]. Meanwhile, the forward velocity obtained from an odometer can be used as a pseudo-measurement.…”

Section: Odometer-aided Bssd Ppp/ins Tightly-coupled Integrationmentioning

confidence: 99%

Low-Cost IMU and Odometer Tightly Augmented PPP-B2b-Based Inter-Satellite Differenced PPP in Urban Environments

Min

Гао

et al. 2022

Remote Sensing

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Since 23 June 2020, BDS-3 has been entirely operated and obtained the ability of global PNT (Positioning, Navigation, and Timing) services. Afterward, real-time Precise Point Positioning (PPP) service is available in China’s surrounding areas via BDS-3 PPP-B2b signal. However, such a real-time PPP service cannot maintain the high accuracy and continuity of positioning solutions in challenging environments, such as urban environments. For that, we carried out a model by integrating between-satellite single-differenced (BSSD) PPP, a low-cost Inertial Navigation System (INS), and an odometer via an extended Kalman filter. The performance of this integration model was assessed with vehicle-borne data. Results demonstrated that (1) the position RMS (Root Mean Square) of BSSD PPP are 64.33 cm, 53.47 cm, and 154.11 cm. Compared with BSSD PPP, about 31.2%, 23.3%, and 27.3% position improvements can be achieved by using INS. Further enhancements of position RMS benefiting from the odometer are 1.34%, 1.41%, and 1.73% in the three directions. (2) Anyway, the accuracy of BSSD PPP/INS/Odometer tightly coupled integration is slightly higher than that of undifferenced PPP/INS/Odometer integration, with average improvement percentages of 7.71%, 3.09%, and 0.27%. Meanwhile, the performance of BSSD PPP/INS/Odometer integration during the periods with satellite outages is better than the undifferenced PPP-based solutions. (3) The improvements in attitudes from an odometer are more significant on heading angle than the other two attitudes, with percentages of 25.00%. (4) During frequent GNSS outage periods, the reduction in average maximum position drifts provided by INS are 18.01%, 8.95%, and 20.74%. After integrating with an odometer, the drifts can be furtherly decreased by 25.11%, 15.96%, and 20.69%. For attitude, about 41.67% reduction in average maximum drifts of heading angles is obtained.

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BDS PPP/INS Tight Coupling Method Based on Non-Holonomic Constraint and Zero Velocity Update

Cited by 7 publications

References 16 publications

An Improved Autonomous Inertial-Based Integrated Navigation Scheme Based on Vehicle Motion Recognition

An Improved Autonomous Inertial-Based Integrated Navigation Scheme Based on Vehicle Motion Recognition

A Digital Track Map-Assisted SINS/OD Fusion Algorithm for Onboard Train Localization

Low-Cost IMU and Odometer Tightly Augmented PPP-B2b-Based Inter-Satellite Differenced PPP in Urban Environments

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