A Kalman filter algorithm based on exact modeling for FOG GPS/SINS integration

Gu, Shanshan; Liu, Jianye; Zeng, Qinghua; Lyu, Pin

doi:10.1016/j.ijleo.2014.01.063

Cited by 10 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The basic principle of the RTS algorithm is that a fixed interval is selected, and the forward solution is based on the Kalman filter. Then, based on all of the forward solution state variables of the fixed interval, the state of each data node in the fixed interval is reverse estimated, resulting in superior data accuracy to that of the forward filter [39][40][41].…”

Section: Data Smoothing Algorithmmentioning

confidence: 99%

The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

Zhang

Cui

Huang³

2021

Applied Sciences

View full text Add to dashboard Cite

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

show abstract

Section: Data Smoothing Algorithmmentioning

confidence: 99%

The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

Zhang

Cui

Huang³

2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…But in order to simplify the model, some components of the stochastic noise were ignored, such as Quantization noise. In References [ 14 , 15 ], the stochastic errors in an inertial sensor were identified by Allan variance, and an equivalent differential equation representation for each kind of stochastic noise was established. The equivalent differential equation was augmented into the KF of GPS/INS integration.…”

Section: Introductionmentioning

confidence: 99%

Vibration Noise Modeling for Measurement While Drilling System Based on FOGs

Zhang

Wang

Gao

et al. 2017

Sensors

View full text Add to dashboard Cite

Aiming to improve survey accuracy of Measurement While Drilling (MWD) based on Fiber Optic Gyroscopes (FOGs) in the long period, the external aiding sources are fused into the inertial navigation by the Kalman filter (KF) method. The KF method needs to model the inertial sensors’ noise as the system noise model. The system noise is modeled as white Gaussian noise conventionally. However, because of the vibration while drilling, the noise in gyros isn’t white Gaussian noise any more. Moreover, an incorrect noise model will degrade the accuracy of KF. This paper developed a new approach for noise modeling on the basis of dynamic Allan variance (DAVAR). In contrast to conventional white noise models, the new noise model contains both the white noise and the color noise. With this new noise model, the KF for the MWD was designed. Finally, two vibration experiments have been performed. Experimental results showed that the proposed vibration noise modeling approach significantly improved the estimated accuracies of the inertial sensor drifts. Compared the navigation results based on different noise model, with the DAVAR noise model, the position error and the toolface angle error are reduced more than 90%. The velocity error is reduced more than 65%. The azimuth error is reduced more than 50%.

show abstract