Using Allan variance to improve stochastic modeling for accurate GNSS/INS integrated navigation

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

doi:10.1007/s10291-018-0718-x

Cited by 25 publications

(12 citation statements)

References 23 publications

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“…The position and velocity of GNSS antenna phase center are related to those of IMU center as follows [26] r…”

Section: Measurement Modelmentioning

confidence: 99%

“…Hence, better reference information (more accurate ˆi k x ) implies a better capability of detecting faults. However, in robust KF with batch processing [26], only the predicted / 1k k x − and / 1 k k P − are available. It can then be concluded that robust sequential KF could not only detect faults in individual channels, but also possess better detection capability.…”

Section: Robust Estimation Based On Innovation Testmentioning

confidence: 99%

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Tightly Coupled GNSS/INS Integration with Robust Sequential Kalman Filter for Accurate Vehicular Navigation

Dong

Wang

Zhang

et al. 2020

Sensors

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With the development of multi-constellation multi-frequency Global Navigation Satellite Systems (GNSS), more and more observations are available for tightly coupled GNSS/Inertial Navigation System (INS) integration. Concerning the accuracy, robustness, and computational burden issues in the integration, we proposed a robust and computationally efficient implementation. The new tight integration model uses pseudorange, Doppler and carrier phase simultaneously, to achieve the maximum possible navigation accuracy for a single receiver. The resultant high-dimensional observation vector is then processed by a sequential Kalman Filter (KF) to improve the computational efficiency in the measurement update step. Based on the innovation of the sequential KF, a robust estimation method with Gaussian test is further devised to detect and adapt the faults in individual GNSS channels. Two field vehicular tests are conducted to evaluate the performance improvements of the proposed method, compared with loose coupling and conventional tight coupling. Test results in favorable environments indicate that the proposed method can significantly improve the velocity and attitude accuracy by 69.42% and 47.16% over loose coupling and by 64.75% and 30.88% over conventional tight coupling, respectively. Moreover, the computational efficiency is also improved by about 53.09% for the proposed method, compared with batch KF processing. In GNSS challenging environments, the proposed method also shows superiority in terms of velocity and attitude accuracy, and better bridging capability during the GNSS partial or complete outages. These results demonstrate that the proposed method is able to provide a more robust and accurate solution in real-time vehicular navigation.

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“…The position and velocity of GNSS antenna phase center are related to those of IMU center as follows [26] r…”

Section: Measurement Modelmentioning

confidence: 99%

Section: Robust Estimation Based On Innovation Testmentioning

confidence: 99%

Tightly Coupled GNSS/INS Integration with Robust Sequential Kalman Filter for Accurate Vehicular Navigation

Dong

Wang

Zhang

et al. 2020

Sensors

Self Cite

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“…Firstly, inspired by the time sequence processing in data science community, Allan variance (AV) was employed to analyze the MEMS IMU error components, and then ARMA models are employed for modeling and representing the noise [22][23][24][25][26][27][28][29][30][31]. After this, some machine learning methods are also employed in this application, for instance, neural networks and support-vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

“…After this, some machine learning methods are also employed in this application, for instance, neural networks and support-vector machine (SVM). With the rapid development of the semiconductor technology and computing capacity, recently, deep learning (DL) gained a boom in data science community [22][23][24][25][26][27][28][29][30][31]. Artificial intelligence (AI) methods were employed in sequence data processing and obtained great advances while compared with the conventional machine learning methods [22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, inspired by the excellent capability of deep neural networks in sequence data processing, the advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed and tested for reducing the MEMS gyroscope noise (only the gyroscope was tested using the proposed method, and the accelerometers processing was similar to that of gyroscope). A commercial MEMS IMU STIM300 was employed in the experiments [22][23][24][25][26][27][28][29][30][31]. e sampling frequency was 125 Hz, and the testing time was approximately 800 seconds.…”

Section: Introductionmentioning

confidence: 99%

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A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network

Zhu

Jiang

2019

Mathematical Problems in Engineering

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Inertial measurement unit (IMU) (an IMU usually contains three gyroscopes and accelerometers) is the key sensor to construct a selfcontained inertial navigation system (INS). IMU manufactured through the Micromechanics Electronics Manufacturing System (MEMS) technology becomes more popular, due to its smaller column, lower cost, and gradually improved accuracy. However, limited by the manufacturing technology, the MEMS IMU raw measurement signals experience complicated noises, which cause the INS navigation solution errors diverge dramatically over time. For addressing this problem, an advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed in the MEMS gyroscope noise suppressing. NAS-RNN was the recently invented artificial intelligence method for time series problems in data science community. Different from conventional method, NAS-RNN was able to search a more feasible architecture for selected application. In this paper, a popular MEMS IMU STIM300 was employed in the testing experiment, and the sampling frequency was 125 Hz. e experiment results showed that the NAS-RNN was effective for MEMS gyroscope denoising; the standard deviation values of denoised three-axis gyroscope measurements decreased by 44.0%, 34.1%, and 39.3%, respectively. Compared with the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), the NAS-RNN obtained further decreases by 28.6%, 3.7%, and 8.8% in standard deviation (STD) values of the signals. In addition, the attitude errors decreased by 26.5%, 20.8%, and 16.4% while substituting the LSTM-RNN with the NAS-RNN.

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Design and Realization of Electronic Fence Based on GPS Technology

Liu

2020

Lecture Notes in Electrical Engineering

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Using Allan variance to improve stochastic modeling for accurate GNSS/INS integrated navigation

Cited by 25 publications

References 23 publications

Tightly Coupled GNSS/INS Integration with Robust Sequential Kalman Filter for Accurate Vehicular Navigation

Tightly Coupled GNSS/INS Integration with Robust Sequential Kalman Filter for Accurate Vehicular Navigation

A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network

Design and Realization of Electronic Fence Based on GPS Technology

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