An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System

Niu, Zun; Li, Guangchen; Guo, Fei; Shuai, Qiangqiang; Zhu, Biao

doi:10.3390/rs14102449

Cited by 7 publications

(3 citation statements)

References 55 publications

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“…The loosely coupled integration of DGNSS/INS was realized by robust adaptive filtering software using the observation noise covariance matrix from experiment 3.1. Four different schemes were designed for this dataset: Method 1 utilized the Extended Kalman Filter (EKF) [39], Method 2 employed the Adaptive Kalman Filter (AKF) [40], Method 3 employed the Robust Kalman Filter (RKF) [39], and Method 4 was based on the proposed Robust Adaptive Filtering with Measurement Noise Covariance Matrix (RAKF). The high-precision navigation solution obtained through the tightly coupled integration of GNSS/INS and post-processing smoothing was used as a reference ground truth.…”

Section: Vehicle Experimental 1 Resultsmentioning

confidence: 99%

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

Yin,

Yang,

et al. 2023

Remote Sensing

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Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation Systems (INS) have been widely applied in many Intelligent Transport Systems. However, due to the influence of various factors, such as complex urban environments, etc., accurately describing the measurement noise statistics of GNSS receivers and inertial sensors is difficult. An inaccurate definition of the measurement noise covariance matrix will lead to the rapid divergence of the position error of the integrated navigation system. To overcome this problem, this paper proposed a Robust Adaptive Extended Kalman Filter (RAKF) method based on an improved measurement noise covariance matrix. By analyzing and considering the position accuracy factors, measurement factor, and position standard deviation in GNSS measurement results, this paper constructed the optimal measurement noise covariance matrix. Based on the Huber model, this paper constructed a two-stage robust adaptive factor expression and obtained the robust adaptive factors with and without abnormal disturbances. And robust adaptive filtering was carried out. To assess the performance of this method, the author conducted experiments on land vehicles by using a self-developed POS system (GNSS/INS combined navigation system). The classic Extended Kalman Filter algorithm (EKF), Adaptive Kalman Filter (AKF) algorithm, Robust Kalman Filter (RKF) algorithm, and the proposed method were compared through data processing. Experimental results show that compared with the classical EKF, AKF, and RKF, the positioning accuracies of the proposed method were improved by 72.43%, 2.54%, and 47.82%, respectively, in the vehicle land experiment. In order to further evaluate the performance of this method, the vehicle data were subjected to different times and degrees of disturbance experiments. Experimental results show that compared with EKF, AKF, and RKF, the heading angle accuracy had obvious advantages, and its accuracy was improved by 34.65%, 31.53%, and 18.36%, respectively. Therefore, this method can effectively monitor and isolate disturbance and improve the robustness, reliability, accuracy, and stability of GNSS/INS integrated navigation systems in complex urban environments.

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Section: Vehicle Experimental 1 Resultsmentioning

confidence: 99%

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

Yin,

Yang,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

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“…The magnetometer output data is sliding mean filtered to perform heading estimation with accelerometer to realize the function of electronic compass. The gyroscope output data is sliding mean filtered to solve the heading angle, and the two solved heading angles are then passed through a complementary filter [8] [9] [10]. The complementary filter synthesizes a complementary result based on two inputs by assigning two reasonable weighting coefficients to the two parts of the input, which are usually both greater than zero and sum to 1.…”

Section: Heading Estimationmentioning

confidence: 99%

Personnel Localization of Real-Time Kinematic Based on Pedestrian Heading Projection Compensation in Substation Signal Interference Environment

Zhang¹,

Zhou²,

Wang³

et al. 2023

JST

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To address the intermittent positioning and drift of personnel positioning RTK in the high-frequency signal interference environment of substations, we propose to use IMU as the positioning compensation module of RTK and adopt the joint RTK/PDR positioning method to solve the positioning results. The heading angle is easily scattered in the pedestrian heading projection (PDR) process and the heading angles calculated from the output data of the gyroscope, accelerometer and magnetometer after denoising are input into the complementary filter for fusion. To improve the accuracy of step estimation in the PDR process, an improved step estimation model is used. For RTK/PDR data fusion, the extended Kalman filter (EKF) method is used, which helps to achieve outdoor full-scene high-accuracy positioning. The final simulation results show that RTK can be effectively compensated by PDR under the interference of high-frequency signals, and the positioning accuracy reaches 0.02 m.

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“…R 1 is the code phase measurement noise, and R 2 is the carrier phase measurement noise. The CNR for each channel is calculated by the variance sum method on the output of the 20ms coherent integration [37],…”

mentioning

confidence: 99%

A robust integrated navigation optimization method for USV in signal occlusion environment

Lou,

Liu,

et al. 2024

Phys. Scr.

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Unmanned surface vehicles (USV) can use global navigation satellite systems (GNSS) and inertial navigation systems (INS) for combined positioning and navigation. However, buildings such as port facilities and bridges blocking GNSS signals will increase the error in the discriminator output in the GNSS vector tracking loop and reduce positioning accuracy. Meanwhile, due to the cumulative error in the inertial navigation system, the credibility of the navigation results when the signal is blocked is further reduced. In this regard, this study proposes a robust integrated navigation optimization method. Specifically, the RTS smoothing optimized Kalman filter is used to constrain the carrier phase error and code phase error output by the discriminator, which can dynamically adjust the gain of the vector tracking loop, thereby improving the signal tracking capability. Simultaneously, the prediction results of the gated recurrent unit (GRU) network optimized based on the attention mechanism are combined with the inertial navigation system to improve navigation accuracy. Furthermore, an adaptive Kalman filter is utilized as the integrated navigation filter. The actual path of the carrier refers to the navigation solution of the existing receiver. In the open environment, the proposed optimization method reduces horizontal positioning error and speed error by 44.7% and 37.1% respectively compared with existing methods. Simultaneously, it can effectively improve the robustness of positioning in signal obstruction environments. The proposed integrated navigation method provides new possibilities for optimizing USV navigation solutions.

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An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System

Cited by 7 publications

References 55 publications

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

Personnel Localization of Real-Time Kinematic Based on Pedestrian Heading Projection Compensation in Substation Signal Interference Environment

A robust integrated navigation optimization method for USV in signal occlusion environment

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