INS/magnetometer integrated positioning based on neural network for bridging long-time GPS outages

Wu, Zongkai; Wang, Wei

doi:10.1007/s10291-019-0877-4

Cited by 22 publications

(12 citation statements)

References 14 publications

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“…A hybrid prediction method which combines the radial basis function neural network (RBFNN) and the time series analysis was proposed to predict the measurement update of Kalman filter during GPS outages in [34]. To improve the positioning precision of INS/GPS integration for long time GPS outages, a magnetometer-assisted positioning solution which uses the RBFNN predictor was introduced [35]. Although these approaches adopted different prediction model, compared with the pure INS mode, they can improve the positioning precision effectively.…”

Section: A Measurement Reconfiguration Based On Rbfnnmentioning

confidence: 99%

Adaptive Fault Isolation and System Reconfiguration Method for GNSS/INS Integration

et al. 2020

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Appropriate fault isolation and system reconfiguration scheme is of great significance to ensure the reliability and precision of the tightly coupled global navigation satellite system/inertial navigation system (GNSS/INS). Currently, the commonly used methods include fault isolation method and fault adaptation method. However, the performance of them is not compared and analyzed fully in the existing literatures. In this paper, the principle of them is analyzed and the performance of them under different conditions is compared in theory firstly. On this basis, to improve the effectiveness and adaptability of fault detection and isolation, an adaptive fault isolation and system reconfiguration method is proposed. The radial basis function neural network (RBFNN) is used to predict the pseudo-GNSS measurement for the measurement reconfiguration. Besides, taking the variety of observation conditions into consideration, an adaptive adjustment criterion is introduced to realize the switch of fault isolation and measurement reconfiguration. The performance of the proposed method is verified and compared with the traditional methods by using the field test data. The results show that the fault isolation method can obtain higher filtering precision than the fault adaptation method in theory, compared with these two methods, the proposed method has better adaptability to the complex environment, and can improve the reliability and precision of the navigation system effectively.

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Section: A Measurement Reconfiguration Based On Rbfnnmentioning

confidence: 99%

Adaptive Fault Isolation and System Reconfiguration Method for GNSS/INS Integration

et al. 2020

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“…The loosely integrated inertial navigation system (INS) and global navigation satellite system (GNSS) corrects INS errors and helps INS to complete navigation tasks by providing the velocity and position of the GNSS. However, GNSS signals are extremely weak when they reach the ground, hence, the signals are vulnerable to interference [1] such as radio signals that fall into the navigation signal pass band and multipath interference caused by reflection, scattering [2] or obstruction from buildings, tunnels and trees [3]. All of these interferences can lead to unknown noise statistics in GNSS navigation parameters.…”

Section: Introductionmentioning

confidence: 99%

A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference

2021

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In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the Gaussian distribution with time-varying mean value than to the Inv-Gamma or Inv-Wishart distribution found by Kullback–Leibler divergence. Therefore, we assumed the prior distribution of measurement noise covariance matrices as Gaussian, and calculated the Gaussian parameters by the black box variational inference method. Finally, we obtained the measurement noise covariance matrices by using the Gaussian parameters. The experimental results illustrate that the proposed algorithm performs better in resisting time-varying measurement noise than the existing Variational Bayesian adaptive filter.

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“…Usually robot positioning is achieved by a single sensor, such as Global Positioning System (GPS), Radio Frequency Identification (RFID), Inertial Navigation System (INS), and so on. But using only a single sensor can’t get rid of its inherent drawbacks when used, for example, GPS is the most widely used positioning sensor Wu ( 2019 ). In the case of indoor positioning, it is difficult to complete positioning due to difficulty in receiving positioning signals Wheeler ( 2018 ).…”

Section: Introductionmentioning

confidence: 99%

LiDAR/DR-Integrated Mobile Robot Localization Employing IMM-EKF/PF Filtering

Feng

Zhang

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In order to solve the problems that indoor mobile robots have parking during the traveling process and the Extended Kalman filter (EKF) receives too much influence on parameter selection, this paper proposes an Interacting Multiple Model (IMM)-EKF/Particle Filtering (PF) adaptive algorithm for the tightly inertial navigation system (INS)/Light Detection And Ranging (LiDAR) integrated navigation. The EKF and PF calculate the position of the robot respectively, then the smaller Mahalanobis distance-based filter’s output is selected as the initial value of the next iteration, which improves the accuracy of the positioning for the robot. Based on that, the two motion equations of the static and normal motion models are dsigned at the same time. A Markov chain for converting the two state of the model, and the weighting filtering result of the filtered is used to provide distance estimates. The real experimental results show that the IMM-EKF/PF adaptive algorithm improves the positioning accuracy of mobile robots in the presence of parking.

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INS/magnetometer integrated positioning based on neural network for bridging long-time GPS outages

Cited by 22 publications

References 14 publications

Adaptive Fault Isolation and System Reconfiguration Method for GNSS/INS Integration

Adaptive Fault Isolation and System Reconfiguration Method for GNSS/INS Integration

A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference

LiDAR/DR-Integrated Mobile Robot Localization Employing IMM-EKF/PF Filtering

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