Improved GNSS vector tracking loop to enhance the navigation performance of USV

Liu, Wei; Huang, Hua; Hu, Yuan; Mou, Minghui; Hsieh, Tsung-Hsuan; Hu, Qingsong; Wang, Shengzheng

doi:10.1016/j.oceaneng.2022.111865

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…T 0 is the coherent integration time and it is 20 ms. C/N 0 is the CNR of the corresponding channel and it varies with the environment. The method of CNR estimation is based on the variance sum method [2,18]. C/N 0 is estimated as follows:…”

Section: Mckf-based Signal Tracking Loopmentioning

confidence: 99%

“…Figure 15 shows the change in code frequency, which is an important parameter related to positioning. In addition, the VTL differs from the traditional STL in that the code frequency can be estimated using the navigation filter [2], which can achieve better tracking performance in the code loop in challenging environments, as follows: where ρi k is the prediction of pseudo-range at moment k. ρi k+1 is at moment k + 1. T is the navigation filter's update interval.…”

Section: Tracking Loop Analysismentioning

confidence: 99%

“…The traditional VT methods use the extended Kalman filter (EKF) navigation filter for positioning. Guo and Liu proposed to improve the VT using the adaptive EKF [7] and the backward smoothing-based unscented Kalman filter (KF) [2], respectively, to enhance navigation filter performance of GNSS receivers. The code tracking performance of the vector tracking loop (VTL) has been improved to varying degrees.…”

Section: Introductionmentioning

confidence: 99%

“…(1) A pre-filter is proposed by introducing MCC into the traditional four-state cascaded incoherent VTL of a GNSS receiver. (2) The measurement noise covariance matrix of the pre-filter is estimated using the CNR obtained from the VTL. The proposed pre-filter can further adjust the measurement noise covariance matrix by identifying the measurement outliers of the pre-filter.…”

Section: Introductionmentioning

confidence: 99%

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A MCKF-based cascade vector tracking method designed for ship navigation

Liu,

Ma,

et al. 2024

Meas. Sci. Technol.

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In ship navigation, the most widely used technology is Global Navigation Satellite Systems (GNSS), which provide the ship’s position and velocity continuously over a period of time. However, when ships are blocked by port buildings or bridges, the quality of signals received from shipborne GNSS receivers may be reduced, resulting in inaccurate ship positioning that poses a risk to navigational safety. In an occluded environment, the measurement process during the signal processing of shipborne GNSS receivers is nonstationary and prone to measurement anomalies, which can contaminate measurement noise with outliers. To address this problem, a cascaded non-coherent vector tracking loop (VTL) is designed, with the Maximum correntropy Kalman filter (MCKF) serving as a cascaded carrier/code pre-filter for shipborne GNSS receivers. The measurement noise covariance matrix of the pre-filter is adaptively calculated and corrected using the carrier-to-noise ratio (CNR) and the maximum correntropy criterion, respectively. The algorithm proposed is more sensitive to outliers than the traditional tracking methods and can effectively solve the state estimation problem under the condition of measurement anomalies. Specifically, the algorithm offers ships with more precise position and velocity estimations and lower signal tracking errors than traditional tracking methods under both static and dynamic conditions, as demonstrated by shipboard experiments. The horizontal positioning is increased by 88.4% and the horizontal velocity error is reduced by 62.1% in the occluded environment under dynamic conditions.

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Section: Mckf-based Signal Tracking Loopmentioning

confidence: 99%

Section: Tracking Loop Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A MCKF-based cascade vector tracking method designed for ship navigation

Liu,

Ma,

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…In order to enhance the accuracy and stability of navigation systems in GNSS-challenged environments, researchers have proposed various approaches, such as the enhancement of tracking loop architectures or the integration of GNSS with inertial sensors [17][18][19]. Multiple structures based on Kalman filters have been proposed for signal tracking in GNSS receiver VTLs.…”

Section: Introductionmentioning

confidence: 99%

Enhancing USVs navigation based on minimum error entropy of GPS vector tracking

Liu,

Chen,

et al. 2024

Meas. Sci. Technol.

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In recent years, Unmanned Surface Vehicles (USVs) have increasingly been used for river monitoring and hydrological surveys. USVs rely on global navigation satellite systems (GNSS) for navigation. However, signal blocking can cause the traditional GNSS vector tracking loop to increase the code phase and carrier frequency errors, leading to higher positioning errors that do not meet USVs’ requirements. To address this problem, we propose a vector tracking method based on the minimum error entropy (MEE) in the signal tracking module. The minimum error entropy Kalman filter (MEEKF) is adopted as the loop filter to mitigate code phase and carrier frequency errors, reduce non-Gaussian noise and random errors generated by signal blocking, and enhance the positioning accuracy and robustness of USV navigation. The measurement noise covariance of the loop filter was adjusted adaptively using the signal carrier-to-noise ratio (CNR). A field experiment was conducted using a commercial GNSS receiver as reference. The results demonstrate a 19.3% improvement in positioning accuracy compared with the traditional method in an open environment. Moreover, the proposed method maintains stable operation and achieves a 79.4% improvement in positioning accuracy during signal blocking. This novel algorithm offers a new concept for USV navigation systems to cope with signal blocking.

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An overview of developments and challenges for unmanned surface vehicle autonomous berthing

Wu,

Li,

Ding

et al. 2023

Complex Intell. Syst.

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With the continuous progress of contemporary science and technology and the increasing requirements for marine vehicles in various fields, the intelligence and automation of ships have become a general trend. The autonomous control of surface Unmanned Surface Vessel (USV) generally covers the USV path planning, path tracking control, and autonomous collision avoidance control. But in the whole navigation process of USV, autonomous berthing is also a crucial part. And the research on the algorithm of the automatic berthing process of the USV is less. Mature USV autonomous berthing technology can effectively reduce the cost of human and material resources and financial resources while reducing the accident rate reasonably and safely. Therefore, it is of great importance to comprehensively promote the development of USV autonomous berthing technology.

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Improved GNSS vector tracking loop to enhance the navigation performance of USV

Cited by 8 publications

References 24 publications

A MCKF-based cascade vector tracking method designed for ship navigation

A MCKF-based cascade vector tracking method designed for ship navigation

Enhancing USVs navigation based on minimum error entropy of GPS vector tracking

An overview of developments and challenges for unmanned surface vehicle autonomous berthing

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