An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module

Yan, Peihui; Jiang, Jinguang; Zhang, Fangning; Xie, Dongpeng; Wu, Jiaji; Zhang, Chao; Tang, Yanan; Liu, Jingnan

doi:10.3390/rs13214317

Cited by 15 publications

(9 citation statements)

References 33 publications

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“…The size of R k reflects the size of the measurement noise. According to equation (14), in the innovation-based adaptive Kalman filtering algorithm, the value of R k will change with the change of innovation v k and time t. Therefore, in the kth moment and the k − 1th moment, the change of the innovation |v k − v k−1 | can reflect whether the measurement noise appears abnormal, if the value of the change is larger, it means that the measurement signal is subject to the greater noise interference.…”

Section: Introduction Of Window Opening Methodmentioning

confidence: 99%

“…Wang proposed an adaptive federal Kalman filtering algorithm based on variable fractional Bayes in the literature [13] and applied it to an INS/GPS/topographic/geomagnetic multi-source navigation system, which was experimentally shown to improve the positioning accuracy relative to the conventional federal filter under time-varying or even unknown MNCM conditions. To address the vulnerability of Global navigation satellite system (GNSS) receivers in urban environments and the low power consumption of integrated navigation systems, Yan proposed an improved robust adaptive Kalman filtering algorithm for single-frequency GNSS/MEMS-IMU/odometer integrated navigation modules with good real-time performance and low computational complexity in the literature [14]. Wang proposed a multifading factor-based adaptive Kalman filtering algorithm in the literature [15], which uses a time-varying adaptive fading factor to modify the filter parameters online and design a Kalman filter for the SINS/GPS system to enhance the robustness of the system under extreme external environments.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Wang

Cheng

et al. 2023

Meas. Sci. Technol.

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Traditional adaptive Kalman filtering algorithms based on innovation are often used to solve the problem of reduced or even divergent filtering estimation accuracy under abnormal measurement noise. However, these algorithms are usually characterized by difficulties in selecting window width and window weight, which cannot simultaneously take into account the filtering tracking sensitivity and filtering accuracy.In this paper, an adaptive Kalman filtering algorithm based on maximum likelihood estimation is proposed, which determines the window size and window weight size under the kth moment by designing a window adaptive selection function and a weight function to change the innovation covariance at the kth moment, which in turn changes the measurement noise covariance at the kth moment, so that the measurement noise covariance is no longer a fixed single value, but can better adapt to the changes in the environment, reflecting good adaptive characteristics. The simulation results based on GPS/SINS integrated navigation system demonstrate that the new filtering algorithm of this paper reflects higher filtering accuracy and stronger robustness under the carrier in multiple motion states and accompanied by time-varying measurement noise interference. Compared with the traditional adaptive Kalman filtering algorithm based on innovation, the accuracy of attitude angle estimation error under this method is improved by 119.97%; the accuracy of velocity estimation error is improved by 264.42%; the accuracy of position estimation error is improved by 156.69%.

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Section: Introduction Of Window Opening Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Wang

Cheng

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…From the aforementioned research, it can be concluded that the quality of observation values of low-cost smart terminals is poor, and abnormal data processing needs to be performed in terms of data preprocessing and quality control to improve positioning performance. In view of that, this research studied robust estimation in smart terminal data processing [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Robust Adaptive RTK Positioning of Low-Cost Smart Terminals

Zhu,

Fan,

et al. 2024

Sensors

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The performance of low-cost smart terminals is limited by the performance of their low-cost Global Navigation Satellite System (GNSS) hardware and chips, as well as by the impact of complex urban environments, which affect the positioning accuracy and stability of GNSS services. To this end, this paper proposes a robust adaptive Kalman filter for different environments that can be applied after data preprocessing. Based on the Kalman filter algorithm, a robust estimation approach is introduced into real-time kinematic (RTK) positioning to make judgments on the abnormal observation values of low-cost smart terminals, which amplifies the variance and covariance of the outlier observation equation, and reduces the impact of outliers on positioning performance. The Institute of Geodesy and Geophysics III (IGG III) function is used for regulation purposes, where prior information is modified and refreshed using the equivalent weight matrix and adaptive factors, thus reducing the impact of system model errors on system state estimation results. In addition, a robust factor is defined to adjust positioning deviation weighting between the pre- and post-test robust estimates. The experimental results show that after robust RTK positioning in the static experiments, the overall improvement in positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and low-cost M8 receiver reached 29.6%, 31.3%, 32.1%, and 30.7%, respectively. Similarly, after applying the proposed robust method in the dynamic experiments, the overall positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and the low-cost M8 receiver improved by 28.3%, 32.9%, 35.4%, and 26.2%, respectively. The experimental results reveal that an excellent positioning effect of a smartphone is positively correlated with robust RTK positioning performance. However, it is worth noting that when the positioning accuracy reaches a high level, such as the positioning results achieved using low-cost receivers, the robustness performance shows a relatively decreasing trend. This finding suggests that under the condition of high positioning accuracy, the sensitivity of specific positioning equipment to interference sources may increase, resulting in a decline in the effect of robust RTK positioning.

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“…The Mahalanobis distance (MD) is an index for anomaly statistical detection [ 22 ]. The robust estimate approach can be used for robust filtering when the observation value is abnormal, which can successfully lessen the impact of abnormal model deviation and abnormal measurement [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises

Wang

et al. 2023

Sensors

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The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model’s high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target’s maneuvering turn. Second, an improved Sage–Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.

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An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module

Cited by 15 publications

References 33 publications

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Research on Robust Adaptive RTK Positioning of Low-Cost Smart Terminals

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises

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