A Dynamic-Weighted Attenuation Memory Extended Kalman Filter Algorithm and Its Application in the Underwater Positioning

Guo, Bo; Ma, Jianye; Wang, Cui

doi:10.1155/2021/3625362

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The code phase error δτ (chips), carrier phase error δθ (rad), carrier frequency error δf (Hz) and carrier frequency rate error δ ˙f (Hz s −1 ) form the state vector of the proposed VTL method. The discretized state vector and the corresponding measurement equation are as follows [18]: (7) where i indicates the satellite numbering. A total of m satellites are involved in the positioning.…”

Section: System Modelmentioning

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%

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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: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liu,

Ma,

et al. 2024

Meas. Sci. Technol.

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“…The Kalman filter algorithm is usually divided into two stages: prediction and update [25]. The former is to obtain the current epoch according to the pre-set initial value and the previous epoch; the latter is to correct the prediction result according to the actual observation value.…”

Section: Kalman Filter Algorithmmentioning

confidence: 99%

Research on incremental clustering algorithm for big data

Yang

2022

Applied Mathematics and Nonlinear Sciences

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As the scale of data becomes larger and larger, clustering processing, a key step in data mining, has important practical significance. Aiming at the problems of time consumption and high clustering errors when the current clustering algorithms deal with massive and dynamic big data, an incremental clustering algorithm is proposed by taking big data as the research object. By exploring the attribute characteristics of big data, four characteristics such as scale, diversity, high speed and value are summarised. For large-scale data streams that have multiple attributes and are acquired one by one, optimise the setting method of the K-means clustering algorithm category centre point, combine the K-means clustering algorithm and the Kalman filter algorithm and measure the distance between data point pairs. Instead of Mahalanobis distance, an incremental clustering algorithm suitable for big data is constructed. Five data sets are selected to carry out example analysis. The results of the algorithm are verified by the algorithm. The proposed algorithm has obvious advantages in the incremental clustering effect of big data. At the same time, it also has efficient and stable computing performance, which meets the expected design requirements and goals.

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Application of L-M Algorithm in High-Rise Structure System Selection

Chen

Xiaoqi

2023

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)

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A Dynamic-Weighted Attenuation Memory Extended Kalman Filter Algorithm and Its Application in the Underwater Positioning

Cited by 3 publications

References 16 publications

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

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

Research on incremental clustering algorithm for big data

Application of L-M Algorithm in High-Rise Structure System Selection

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