Improved forward and backward adaptive smoothing algorithm

Lin, Xu; Yang, Xinchun; Hu, Chihao; Li, Wei

doi:10.1007/s10291-021-01185-0

Cited by 8 publications

(8 citation statements)

References 29 publications

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“…Referring to [30][31][32][33], The basic equations of the discrete form of the backward Kalman filtering are as follows:…”

Section: Backward Kf Designmentioning

confidence: 99%

“…In a two-filter smoothing system, the information provided by the two filters may exhibit different characteristics. By making full use of the resources of both filters, the rational use of the measurement information and the combination of their complementary and redundant information in time based on some optimization criterion can achieve an optimal consistent description of the observed target [29][30][31][32]. The Kalman smoothing algorithm, which is implemented as an information filter, is a good solution to the problems of the two-filter system.…”

Section: Information Fusionmentioning

confidence: 99%

“…The Kalman smoothing algorithm, which is implemented as an information filter, is a good solution to the problems of the two-filter system. The essence of information filtering is to weight the results of the forward and backward KF and average them to obtain higher accuracy filtering results [31,32]. The basic formulas for information fusion are as follows:…”

Section: Information Fusionmentioning

confidence: 99%

“…Although adding a loop KF to filter the phase error can enhance the tracking performance of the VTL for signals in harsh environments, tracking accuracy does not improve significantly. Several researchers have demonstrated that smoothing algorithms can improve the filtering accuracy and robustness of KF-based integrated navigation systems [29][30][31][32]. For example, Lin et al proposed the use of the Kalman smoothing algorithm in a target tracking system, which can effectively suppress the effects of anomalous dynamic models and improve the computational accuracy of the integrated navigation filter [32].…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers have demonstrated that smoothing algorithms can improve the filtering accuracy and robustness of KF-based integrated navigation systems [29][30][31][32]. For example, Lin et al proposed the use of the Kalman smoothing algorithm in a target tracking system, which can effectively suppress the effects of anomalous dynamic models and improve the computational accuracy of the integrated navigation filter [32]. To enhance the signal tracking performance and navigation positioning accuracy of the GNSS receiver of the USV in a signal-challenged environment, this paper added Kalman smoother to the tracking loop to improve the estimation accuracy of the phase error.…”

Section: Introductionmentioning

confidence: 99%

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A robust GNSS sensors in presence of signal blockage for USV application

Liu,

Huang,

et al. 2023

Meas. Sci. Technol.

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Unmanned surface vehicle (USV) can navigate autonomously via the global navigation satellite system (GNSS). However, the traditional GNSS scalar tracking loop easily loses lock in low carrier-to-noise ratio (CNR) situations, such as signal occlusion and weak signals. Meanwhile, an increase in the carrier/code phase error leads to an increase in the measurement error of the navigation filter, which decreases the accuracy of the position estimation. To solve this problem, this paper proposes a carrier and code tracking structure based on a forward and backward Kalman filter to dynamically adjust the gain of the vector tracking loop. The carrier and code phase errors calculated by the loop discriminators were linearly transformed into pseudo-range rate and pseudo-range errors after filtering and smoothing, which were used as the measurements of the navigation filter. The signal CNR was used to adaptively adjust the measurement noise covariance matrix of the loop filters. The field tests used a commercial receiver’s navigation solution as the reference. In the stationary test, the proposed structure reduced the localization error by 44.3% compared with the traditional methods. In kinematic experiments, the proposed structure reduced the carrier and code phase errors in a harsh signal environment and improved the positioning accuracy at the source. The test results demonstrate that the proposed GNSS tracking method can provide a possible solution for the development of navigation systems for USV.

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“…Referring to [30][31][32][33], The basic equations of the discrete form of the backward Kalman filtering are as follows:…”

Section: Backward Kf Designmentioning

confidence: 99%

Section: Information Fusionmentioning

confidence: 99%

Section: Information Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A robust GNSS sensors in presence of signal blockage for USV application

Liu,

Huang,

et al. 2023

Meas. Sci. Technol.

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A modified federated Student’s t-based variational adaptive Kalman filter for multi-sensor information fusion

Qiao,

Fan,

Wang

et al. 2023

Measurement

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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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Improved forward and backward adaptive smoothing algorithm

Cited by 8 publications

References 29 publications

A robust GNSS sensors in presence of signal blockage for USV application

A robust GNSS sensors in presence of signal blockage for USV application

A modified federated Student’s t-based variational adaptive Kalman filter for multi-sensor information fusion

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

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