Evaluation on loosely and tightly coupled GNSS/INS vehicle navigation system

Wang, Jian; Dan, Liu; Jiang, Wei; Lu, Debiao

doi:10.1109/iceaa.2017.8065396

Cited by 15 publications

(12 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 9 shows the improvement of accuracy after applying the proposed method, by comparing to the methods proposed by Smyth and Wu [ 23 ], and loosely and tightly-coupled Kalman filter [ 22 ]. Considering the RMSE of RTK-GNSS displacement measurement is 8.56 mm, the RMSE of the proposed method including MHDR is 5.64 mm, representing a 33.6% reduction.…”

Section: Lab-scale Experimentsmentioning

confidence: 99%

“…Note that the values of Kalman filter parameters are set to the same to the lab-scale test in Section 3 . The estimation accuracy of the proposed method is compared to the ones of the methods proposed by Smyth and Wu [ 23 ], and loosely and tightly-coupled Kalman filter [ 22 ]. Whereas the three previous methods cannot reduce the low-frequency error, inherited from the one of RTK-GNSS displacement measurement.…”

Section: Field Testsmentioning

confidence: 99%

“…Then, two-stage Kalman filter produces the final displacement estimates by correcting high-frequency error and residual low-frequency error. Table 2 compares the performance of the proposed method to the previous Kalman filter based displacement estimation method proposed by Smyth and Wu [ 23 ] and loosely and tightly-coupled Kalman filter [ 22 ], and the proposed method. Since the proposed method, unlike other previous methods, mitigates the errors with both MHDR and Kalman filtering, the accuracy of the estimates is highly enhanced up to 2.69 mm.…”

Section: Field Testsmentioning

confidence: 99%

“… Displacement estimation results and RMSE errors for the Yeongjong Grand bridge: ( a ) the method proposed by Smyth and Wu [ 23 ], ( b ) tightly-coupled and ( c ) loosely-coupled Kalman filter [ 22 ], and ( d ) the proposed method. …”

Section: Figurementioning

confidence: 99%

“…Two data fusion schemes are commonly used—loosely-coupled and tightly-coupled Kalman filters [ 20 , 21 ]. It is known that a tightly-coupled Kalman filter has a better performance in vehicle position estimation than loosely-coupled one [ 22 ]. In the civil engineering field, Kalman filtering has been mainly used for the estimation of structural displacement by multi-rate data fusion of acceleration and displacement measurements.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Kim

Sohn

2020

Sensors

View full text Add to dashboard Cite

In this paper, we propose a dynamic displacement estimation method for large-scale civil infrastructures based on a two-stage Kalman filter and modified heuristic drift reduction method. When measuring displacement at large-scale infrastructures, a non-contact displacement sensor is placed on a limited number of spots such as foundations of the structures, and the sensor must have a very long measurement distance (typically longer than 100 m). RTK-GNSS, therefore, has been widely used in displacement measurement on civil infrastructures. However, RTK-GNSS has a low sampling frequency of 10–20 Hz and often suffers from its low stability due to the number of satellites and the surrounding environment. The proposed method combines data from an RTK-GNSS receiver and an accelerometer to estimate the dynamic displacement of the structure with higher precision and accuracy than those of RTK-GNSS and 100 Hz sampling frequency. In the proposed method, a heuristic drift reduction method estimates displacement with better accuracy employing a low-pass-filtered acceleration measurement by an accelerometer and a displacement measurement by an RTK-GNSS receiver. Then, the displacement estimated by the heuristic drift reduction method, the velocity measured by a single GNSS receiver, and the acceleration measured by the accelerometer are combined in a two-stage Kalman filter to estimate the dynamic displacement. The effectiveness of the proposed dynamic displacement estimation method was validated through three field application tests at Yeongjong Grand Bridge in Korea, San Francisco–Oakland Bay Bridge in California, and Qingfeng Bridge in China. In the field tests, the root-mean-square error of RTK-GNSS displacement measurement reduces by 55–78 percent after applying the proposed method.

show abstract

Section: Lab-scale Experimentsmentioning

confidence: 99%

Section: Field Testsmentioning

confidence: 99%

Section: Field Testsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Kim

Sohn

2020

Sensors

View full text Add to dashboard Cite

show abstract

Survey of connected automated vehicle perception mode: from autonomy to interaction

Shangguan

Guo

et al. 2018

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Performance analysis of GNSS/INS loosely coupled integration systems under GNSS signal blocking environment

Wang

2020

E3S Web Conf.

View full text Add to dashboard Cite

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most widely used navigation systems at present. Aiming at the limitations of a single system application, this paper uses kalman filter to fuse the pose information provided by GNSS and INS, respectively. GNSS has the characteristics of being easily affected by the environment but with high absolute positioning accuracy. INS has the characteristics of high sampling frequency and autonomous navigation, but the error accumulates with time. Combining the advantages of the two systems to achieve the purpose of obtaining higher-precision pose information. In addition, aiming at the problem that GNSS/INS integration cannot provide continuous, stable and reliable navigation solutions under the GNSS signal blocking environment, a smoothing post-processing algorithm for GNSS/INS integration is studied. Through experimental verification, this algorithm can effectively improve the pose accuracy under GNSS signal blocking environment.

show abstract

Evaluation on loosely and tightly coupled GNSS/INS vehicle navigation system

Cited by 15 publications

References 6 publications

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Survey of connected automated vehicle perception mode: from autonomy to interaction

Performance analysis of GNSS/INS loosely coupled integration systems under GNSS signal blocking environment

Contact Info

Product

Resources

About