Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages

Chen, Xiyuan; Shen, Chong; Zhang, Wei-Bin; Tomizuka, Masayoshi; Xu, Yuan; Chiu, Kuan-Lin

doi:10.1016/j.measurement.2013.07.016

Cited by 125 publications

(72 citation statements)

References 21 publications

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“…Updating the particles and the corresponding weights based on STKF 27 Considering the STKF is used as the importance function, the particle and corresponding variance are updated as follows: State variable x k + 1=k is as follows The prediction error s l + 1 is estimated as follows…”

Section: Initial Samplingmentioning

confidence: 99%

Remaining useful life prognostic of power metal oxide semiconductor field effect transistor based on improved particle filter algorithm

Yang

Zhen

et al. 2017

Advances in Mechanical Engineering

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The power metal oxide semiconductor field effect transistor is used extensively in analog circuits and digital circuits. However, it is also the highest failure rate component in the power electronics system. In order to avoid serious failures in the electrical system, it is necessary to predict the remaining useful life of the metal oxide semiconductor field effect transistor. With the aim to accurately predict the remaining useful life, this article presents an improved particle filter method, which can estimate the metal oxide semiconductor field effect transistor degradation degree by the on-resistance (Ron) changing data. This method is based on the particle filter algorithm, which is suitable for nonlinear and non-Gaussian system; at the same time, the proposed method can avoid the degeneracy phenomenon in the particle filter. First, the strong tracking Kalman filter is used as the importance function to update the particles with the latest observation in the sample process. The Metropolis-Hastings algorithm is used to introduce some new particles to guarantee the effective particle number in the resampling process. The experimental results indicate that the improved particle filter algorithm can obtain more effective remaining useful life prognostic than that of the particle filter. KeywordsRemaining useful life, metal oxide semiconductor field effect transistor, strong tracking Kalman filter, prognostic Date

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Section: Initial Samplingmentioning

confidence: 99%

Remaining useful life prognostic of power metal oxide semiconductor field effect transistor based on improved particle filter algorithm

Yang

Zhen

et al. 2017

Advances in Mechanical Engineering

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“…There are some recently published artificial intelligence-based GPS/IMU fusion methods for land vehicle navigation [32], which provide higher accuracies during GPS outages than the achieved accuracy using the proposed method. However, the non-holonomic constraints in the land vehicle navigation (i.e.…”

Section: Performance Under Gps Blockagementioning

confidence: 99%

A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications

et al. 2015

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“…The system model with its initial conditions and noise characteristics must be defined in advance for KF realization. In practical situations, a precisely defined model is unrealistic because it violates the statistical distributions assumptions due to noise description and system models uncertainties (Chen et al 2013). Consequently, the estimation error is accumulated with time.…”

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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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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Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages

Cited by 125 publications

References 21 publications

Remaining useful life prognostic of power metal oxide semiconductor field effect transistor based on improved particle filter algorithm

Remaining useful life prognostic of power metal oxide semiconductor field effect transistor based on improved particle filter algorithm

A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications

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

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