A Wavelet-Based Multisensor Data Fusion Algorithm

Xu, Lijun; Zhang, Jian Qiu; Yan, Yong

doi:10.1109/tim.2004.834066

Cited by 64 publications

(23 citation statements)

References 8 publications

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“…However, the acquisition of a conditional probability density is difficult since it involves a complex computational process. The least-squares method utilizes the mean-square error theory for parameter estimation [16][17][18][19][20][21]. However, this method only uses observation model information at a discrete epoch for state parameter estimation, without involvement of the state model information.…”

Section: Related Workmentioning

confidence: 99%

Random weighting method for estimation of error characteristics in SINS/GPS/SAR integrated navigation system

Gao

Zhong

et al. 2015

Aerospace Science and Technology

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Section: Related Workmentioning

confidence: 99%

Random weighting method for estimation of error characteristics in SINS/GPS/SAR integrated navigation system

Gao

Zhong

et al. 2015

Aerospace Science and Technology

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“…Thus, r ij can be used as an indication of the uncertainty of the velocity measurement. The weighted average velocity of particles is then determined by fusing the individual velocities [14,19].…”

Section: Measurement Principlesmentioning

confidence: 99%

Pulverized coal flow metering on a full-scale power plant using electrostatic sensor arrays

Qian

Huang

et al. 2014

Flow Measurement and Instrumentation

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“…A wavelet transform can be used to estimate the variance of the noise in a fast time-varying signal [34]. If the wavelet is carefully selected, the estimation of the variance may not be influenced by the fluctuation of the fast time-varying signal [35].…”

Section: Estimation Of Measurement Sequence Noisementioning

confidence: 99%

“…This length is termed as the estimation length and denoted by L. The length L depends on the sampling frequency and the response time [35]:…”

Section: Estimation Of Measurement Sequence Noisementioning

confidence: 99%

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

Deng

Chen

2013

J. of Syst. Eng. Electron.

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An adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter (UKF) when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

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A Wavelet-Based Multisensor Data Fusion Algorithm

Cited by 64 publications

References 8 publications

Random weighting method for estimation of error characteristics in SINS/GPS/SAR integrated navigation system

Random weighting method for estimation of error characteristics in SINS/GPS/SAR integrated navigation system

Pulverized coal flow metering on a full-scale power plant using electrostatic sensor arrays

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

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