Integrating Extreme Learning Machine with Kalman Filter to Bridge GPS Outages

Jingsen, Zheng; Zhao, Wenyi; Han, Bo; Yali, Wen

doi:10.1109/icisce.2016.98

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…The proposed method (NARX‐aided UKF) is compared to UKF and two widely adopted methods that use different input configurations in order to validate the selection of the input configurations of NARX networks. The first method (shortly M1) uses the current information of specific force and angular rates

{{\overset{f}{true}}^{B}, {\overset{ω}{true}}_{I B}^{B}}

for estimating the position and velocity errors as in (Chen & Fang, 2014; Jingsen et al., 2016). The second method (shortly M2) uses the four‐step information of specific force, angular rates, velocity and yaw

{{\overset{f}{true}}^{B}, {\overset{ω}{true}}_{I B}^{B}, bold-italicV, ψ}

as in (Fang et al., 2020).…”

Section: Resultsmentioning

confidence: 99%

A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Bitar

Gavrilov

2021

Navigation

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Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel system that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX‐based module is utilized to predict the measurement updates of UKF during GNSS outages. A new offline approach for selecting the optimal inputs of NARX networks is suggested and tested. This approach is based on mutual information (MI) theory for identifying the inputs that influence each of the outputs (the measurement updates of UKF) and lag‐space estimation (LSE) for investigating the dependency of these outputs on the past values of the inputs and the outputs. The performance of the proposed system is verified experimentally using a real dataset. The comparison results indicate that the NARX‐aided UKF outperforms other methods that use different input configurations for neural networks.

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{{\overset{f}{true}}^{B}, {\overset{ω}{true}}_{I B}^{B}}

{{\overset{f}{true}}^{B}, {\overset{ω}{true}}_{I B}^{B}, bold-italicV, ψ}

as in (Fang et al., 2020).…”

Section: Resultsmentioning

confidence: 99%

A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Bitar

Gavrilov

2021

Navigation

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“…Where * is represent the Moore-Penrose generalization of the (H) matrix inverse [30]. This inverse matrix can be calculated utilizing various methods such as orthogonalization, iterative, orthogonal projection and singular value decomposition methods [31].…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19

Hasan¹,

Mahmoud²,

Hasan³

2021

IJIES

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Recently, a new challenge to the researchers has been emerged due to the spread of a new outbreak called novel corona-virus (COVID-19) to portend the confirmed cases. Since discovering the first certain cases in Wuhan, China, the COVID-19 has been widely spreading and expanding to other provinces and other countries via travellers into various countries around the world. COVID-19 virus is not a problem of only developing countries, but also of developed countries. Artificial Intelligence (AI) techniques can be profitable to predict such; parameters, risks and influence of an outbreak. Therefore, accurate prognosis can be helpful to control the spread of such viruses and provide crucial information for identifying the type of virus interventions and intensity. Here are develop an intelligent model depicting COVID-19 transmission and resulting confirmed cases. The epidemic curve of COVID-19 cases was modelled. The main key idea of predicting the confirmed cases is based on two factors 1) cumulative number of confirmed cases and 2) the daily confirmed cases instead of using only one factor as previous research. In this study, a comparison between different intelligent techniques has been conducted. To assess the effectiveness of these intelligent models, a recorded data of 5 months has been used for training in various countries. Results obtained show the superiority of ELM model in accurate prediction and outperforms other intelligent techniques. We have used a Social Spider Optimization (SSO) method to optimize the ELM parameters. The prediction results show the superiority of the proposed intelligent predictors with accuracy greater than 93%. Therefore, medical personnel can take defensive steps earlier.

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“…Similar approaches, which couple neural networks to KF, were proposed to Fuse INS with GPS data [18] [19][20] [21]. In [20] the neural network was mainly used to predict INS errors when GPS is not available.…”

Section: State Of the Artmentioning

confidence: 99%

2D SLAM Correction Prediction in Large Scale Urban Environments

Alsayed¹,

Bresson²,

Verroust-Blondet³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Simultaneous Localization And Mapping (SLAM) is one of the major bricks needed to build truly autonomous mobile robots. The probabilistic formulation of SLAM is based on two models: the motion model and the observation model. In practice, these models, together with the SLAM map representation, do not model perfectly the robot's real dynamics, the sensor measurement errors and the environment. Consequently, systematic errors affect SLAM estimations. In this paper, we propose two approaches to predict corrections to be applied to SLAM estimations. Both are based on the Ensemble Multilayer Perceptron model. The first approach uses successive estimated poses to predict the errors, with no assumptions on the underlying SLAM process or sensor used. The second method is specific to 2D likelihood SLAM approaches, thus, the likelihood distributions are used to predict the corrections, making this second approach independent of the sensor used. We also build a hybrid correction module based on successive estimated poses and the likelihood distributions. The validity of both approaches is evaluated through two experiments using different evaluation metrics and sensor configurations.

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Integrating Extreme Learning Machine with Kalman Filter to Bridge GPS Outages

Cited by 11 publications

References 13 publications

A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19

2D SLAM Correction Prediction in Large Scale Urban Environments

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