GPS/INS Integrated Navigation Based on UKF and Simulated Annealing Optimized SVM

Jiang, Zhuqing; Liu, Chonghua; Zhang, Gong; Wang, Yupeng; Huang, Cheng-Kai; Liang, Jianhui

doi:10.1109/vtcfall.2013.6692217

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…The achieved model estimates GPS signals during its outage to virtually aid integration filter to develop more accurate navigation results and overcome updating shortage. Employing mechanized inertial velocities as model candidates, support vector machine (SVM) was proposed to model GPS velocities to provide its estimates for unscented KF updates during GPS denied conditions (Jiang et al 2013;Xu et al 2012). SVM has been used widely as a regression algorithm for both linear and nonlinear modeling but this approach neglects that the filter is derived by errors which may lead to a growth of the overall system errors.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

Ismail

Abdelkawy

2018

J. Glob. Position. Syst.

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Continuity of accurate navigational data for intelligent transportation applications has been widely provided by utilizing low-cost navigation systems through integrating GPS with micro-electro-mechanical-system (MEMS) inertial sensors. To achieve the required accuracy, augmentation of Kalman filter (KF) with nonlinear error modeling techniques such as fast orthogonal search (FOS) was introduced to enhance the navigational solution by estimating and eliminating a great part of both linear and nonlinear errors of azimuth angle sensed by MEMS gyro. Although this augmented approach enhanced the overall navigational accuracy to some extent, it still suffers from some drawbacks that diverge the system accuracy during GPS long outage periods. These drawbacks stem from the wide-variational behavior and high nonlinearities of the errors in MEMS gyros which make it difficult to depend on the non-adaptive linear error model provided by KF to model the two types of MEMS azimuth errors. In this paper we tried to minimize the effect of uncertainties associated with the KF azimuth prediction during the absence of GPS by introducing a hybrid error model which employs support vector machine (SVM) to model the KF output and FOS, based on autoregressive (AR) concept, to model the nonlinear azimuth errors. The performance of the proposed hybrid SVM-FOS approach is evaluated for GPS/ RISS (Reduced inertial sensor system integrated system) and the results were compared with the conventional KF and augmented KF-FOS approaches.

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Section: Introductionmentioning

confidence: 99%

“…SVM is able to define a model based on small training data (Xu et al 2012) with little training time and hence it is suitable for real time implementation. It shows robustness against overfitting problems (Jiang et al 2013) and does not need priori knowledge of the noise model.…”

Section: Introductionmentioning

confidence: 99%

A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

Ismail

Abdelkawy

2018

J. Glob. Position. Syst.

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“…Vehicular navigation is often characterized with dynamics changes in motion and the use of KF could negatively have an impact on the position accuracy due to its inappropriate stochastic models and its inability to solve nonlinear system problems with variational and colored noise properties [5]. As a consequence of the aforementioned KF drawbacks, different approaches based on Bayesian filtering [6][7][8][9] and artificial intelligence (AI) techniques [3,[10][11][12][13][14][15][16] have been proposed to improve the effectiveness of the integration methodology in bridging GPS outages.…”

Section: Introductionmentioning

confidence: 99%

GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM

Georges

Wang

Xiao

2015

Mathematical Problems in Engineering

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Among the inertial navigation system (INS) devices used in land vehicle navigation (LVN), low-cost microelectromechanical systems (MEMS) inertial sensors have received more interest for bridging global navigation satellites systems (GNSS) signal failures because of their price and portability. Kalman filter (KF) based GNSS/INS integration has been widely used to provide a robust solution to the navigation. However, its prediction model cannot give satisfactory results in the presence of colored and variational noise. In order to achieve reliable and accurate positional solution for LVN in urban areas surrounded by skyscrapers or under dense foliage and tunnels, a novel model combining variational Bayesian adaptive Kalman smoother (VB-ACKS) as an alternative of KF and ensemble regularized extreme learning machine (ERELM) for bridging global positioning systems outages is proposed. The ERELM is applied to reduce the fluctuating performance of GNSS during an outage. We show that a well-organized collection of predictors using ensemble learning yields a more accurate positional result when compared with conventional artificial neural network (ANN) predictors. Experimental results show that the performance of VB-ACKS is more robust compared with KF solution, and the prediction of ERELM contains the smallest error compared with other ANN solutions.

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“…These two methods are easy to achieve but only work when the movement path of the vehicle is simple. So the majority of researches have been focusing on the online study methods using Artificial Neural Network (ANN) or Support Vector Machine (SVM) [6]. Nowadays, several advanced information fusion algorithms have been proposed, such as strong-tracking Kalman Filter (STKF) combined with wavelet neural network (WNN) [7], genetic algorithm based adaptive neurofuzzy inference system (GANFIS) [8], and Dempster-Shafer augmented Support Vector Machine (DS-SVM) [9].…”

Section: Introductionmentioning

confidence: 99%

Error Prediction for SINS/GPS after GPS Outage Based on Hybrid KF-UKF

Zhang

Chu

Sun

et al. 2015

Mathematical Problems in Engineering

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The performance of MEMS-SINS/GPS integrated system degrades evidently during GPS outage due to the poor error characteristics of low-cost IMU sensors. The normal EKF is unable to estimate SINS error accurately after GPS outage owing to the large nonlinear error caused by MEMS-IMU. Aiming to solve this problem, a hybrid KF-UKF algorithm for real-time SINS/GPS integration is presented in this paper. The linear and nonlinear SINS error models are discussed, respectively. When GPS works well, we fuse SINS and GPS with KF with linear SINS error model using normal EKF. In the case of GPS outage, we implement Unscented Transform to predict SINS error covariance with nonlinear SINS error model until GPS signal recovers. In the simulation test that we designed, an evident accuracy improvement in attitude and velocity could be noticed compared to the normal EKF method after the GPS signal recovered.

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GPS/INS Integrated Navigation Based on UKF and Simulated Annealing Optimized SVM

Cited by 6 publications

References 11 publications

A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM

Error Prediction for SINS/GPS after GPS Outage Based on Hybrid KF-UKF

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