Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation

The tightly coupled navigation system is commonly used in UAV products and land vehicles. It adopts the Kalman filter to combine raw satellite observations, including the pseudorange, pseudorange rate and Doppler frequency, with the inertial measurements to achieve high navigational accuracy in GNSS...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 14; číslo 7; s. 1691
Hlavní autoři: Xu, Hongfu, Luo, Haiyong, Wu, Zijian, Wu, Fan, Bao, Linfeng, Zhao, Fang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2022
Témata:
Accuracy
Adaptability
adaptive Kalman filtering
Algorithms
Covariance
Environmental degradation
Global navigation satellite system
Kalman filters
measurement noise estimation
Methods
Natural language processing
Navigation systems
Neural networks
Noise measurement
Noise prediction
Noise reduction
Remote sensing
Satellite observation
Satellites
Sensors
tightly coupled integrated navigation
transformer
Transformers
Unmanned aerial vehicles
ISSN:2072-4292, 2072-4292
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Shrnutí:The tightly coupled navigation system is commonly used in UAV products and land vehicles. It adopts the Kalman filter to combine raw satellite observations, including the pseudorange, pseudorange rate and Doppler frequency, with the inertial measurements to achieve high navigational accuracy in GNSS-challenged environments. The accurate estimation of measurement noise covariance can ensure the quick convergence of the Kalman filter and the accuracy of the navigation results. Existing tightly coupled integrated navigation systems employ either constant noise covariance or simple noise covariance updating methods, which cannot accurately reflect the dynamic measurement noises. In this article, we propose an adaptive measurement noise estimation algorithm using a transformer and residual denoising autoencoder (RDAE), which can dynamically estimate the covariance of measurement noise. The residual module is used to solve the gradient degradation problem. The DAE is adopted to learn the essential characteristics from the noisy ephemeris data. By introducing the attention mechanism, the transformer can effectively learn the time and space dependency of long-term ephemeris data, and thus dynamically adjusts the noise covariance with the predicted factors. Extensive experimental results demonstrate that our method can achieve sub-meter positioning accuracy in the outdoor open environment. In a GNSS-degraded environment, our proposed method can still obtain about 3 m positioning accuracy. Another test on a new dataset also confirms that our proposed method has reasonable robustness and adaptability.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs14071691