Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages

Wang, Jian; Han, Hyangsun; Meng, Xiaolin; Yao, Lihui; Li, Zengke

doi:10.1080/00396265.2016.1190162

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…Moreover, INS outputs are always exposed to high noise and different types of uncertainties, such as bias, scale factor, and misalignment. Therefore, inconsistency between vehicle and sensors lead to INS gross errors [25,26]. Availability of correct and persistent GPS data as observation is essential to continue correct estimation in integrated navigation systems.…”

Section: Robust Kalman Filtermentioning

confidence: 99%

ANN-assisted robust GPS/INS information fusion to bridge GPS outage

Aslinezhad

Malekijavan

Abbasi

2020

J Wireless Com Network

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Inertial navigation is an edge computing-based method for determining the position and orientation of a moving vehicle that operates according to Newton's laws of motion on which all the computations are performed at the edge level without need to other far resources. One of the most crucial struggles in Global Positioning System (GPS) and Inertial Navigation System (INS) fusion algorithms is that the accuracy of the algorithm is reduced during GPS interruptions. In this paper, a lowcost method for GPS/INS fusion and error compensation of the GPS/INS fusion algorithm during GPS interruption is proposed. To further enhance the reliability and performance of the GPS/INS fusion algorithm, a Robust Kalman Filter (RKF) is used to compensate the influence of gross error from INS observations. When GPS data is interrupted, Kalman filter observations will not be updated, and the accuracy of the position will continuously decrease over time. To bridge GPS data interruption, an artificial neural network-based fusion method is proposed to provide missing position information. A well-trained neural network is used to predict and compensate the interrupted position signal error. Finally, to evaluate the effectiveness of the proposed method, an outdoor test using a custom-designed hardware, GPS, and INS sensors is employed. The results indicate that the accuracy of the positioning has improved by 67% in each axis during an interruption. The proposed algorithm can enhance the accuracy of the GPS/INS integrated system in the required navigation performance.

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Section: Robust Kalman Filtermentioning

confidence: 99%

ANN-assisted robust GPS/INS information fusion to bridge GPS outage

Aslinezhad

Malekijavan

Abbasi

2020

J Wireless Com Network

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“…Even if the observation is reliable, it still requires more accurate dynamic model for both INS and GPS errors, since it is usually difficult to set a certain stochastic model for each inertial sensor that works efficiently in all environments [ 28 ]. In a low-cost GNSS/MINS integrated system, inertial sensor also includes gross error due to unmeasurable external disturbances and high dynamics which against stochastic model, and may be harmful for state prediction vector and its covariance [ 6 ]. So an adaptive Kalman filter based on state prediction covariance should be constructed to adjust the contribution of the predicted states from the IMU sensors.…”

Section: Optimal Rbf Neural Network Aided Robust Kalman Filtermentioning

confidence: 99%

“…Kalman filtering has been applied for many years to provide an optimal GPS/INS integrated module [ 4 , 5 , 6 ]. However, on the one hand, the system performance of a GNSS/INS integrated LVNS entering a tunnel, a downtown area with high buildings, a canyon or a forest may frequently be degrade, bringing gross observation errors [ 4 ] into filtering.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, MEMS IMU outputs are corrupted with high noise and large uncertainties, such as bias, scale factor and non-orthogonalities [ 5 ]. Meanwhile, dynamic inconsistency between vehicle and sensors also lead to IMU gross errors [ 6 ]. Unlike observation gross errors, this case may bring dramatic dynamic model errors into filtering so the estimation is very different from the real dynamic system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

Ning

Wang

Han

et al. 2018

Sensors

Self Cite

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Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors. In the proposed method, firstly a test statistic based on Mahalanobis distance is treated as judging index to achieve fault detection. Then, an optimal RBF neural network strategy is trained on-line by the optimality principle. The network’s output will bring benefits in recognizing the above two kinds of filtering fault and the system is able to choose a robust or adaptive Kalman filtering method autonomously. A field vehicle test in urban areas with a low-cost GNSS/INS integrated system indicates that two types of errors simulated in complex urban areas have been detected, distinguished and eliminated with the proposed scheme, success rate reached up to 92%. In particular, we also find that the novel neural network strategy can improve the overall position accuracy during GNSS signal short-term outages.

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“…ey proposed a tightly coupled GPS/BDS/INS integration scheme based on robust wavelet and introduced GPS/BDS doubledifference (DD) carrier phase and pseudorange measurements to construct a 27-state tightly coupled GPS/BDS/INS integral equation. eir research lacks innovation [4].…”

Section: Introductionmentioning

confidence: 99%

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Zhang

2021

Mathematical Problems in Engineering

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Aerobics is one of the main contents of physical education, which has a positive role in promoting the health of young people. This paper mainly studies the parallel processing method of inertial aerobics multisensor data fusion. In this paper, an aerobics exercise system is designed, which uses digital filter to remove the noise generated in the process of exercise. In this paper, Kalman filter is used to filter the pulse error of accelerometer, and the data structure of unidirectional link is used to achieve the effect of sliding window, which can reduce the memory cost to the greatest extent. In this paper, the region of moving object is determined by horizontal and vertical projection of binary symmetric difference image. At the same time, the optimal feature combination is selected from the reduced features by feature subset selection, and the classification algorithm is used as the evaluation function in the optimization process. Finally, the collected data are tested, analyzed, and sorted out. The experimental data show that, after calibrating the sensor data, the static x-axis and y-axis data are about 0 g, and the z-axis data are about 1 g, which is closer to the real value. The results show that the attitude data collected by the inertial sensor can be stably transmitted to the software of the computer wirelessly for attitude reconstruction, and the recognition of each attitude and parameter has reached a high accuracy.

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Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages

Cited by 4 publications

References 15 publications

ANN-assisted robust GPS/INS information fusion to bridge GPS outage

ANN-assisted robust GPS/INS information fusion to bridge GPS outage

An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

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