Shaping filter representation of nonstationary colored noise

Brandenburg, L.; Meadows, H. E.

doi:10.1109/tit.1971.1054585

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…In some applications, such as astronomical image restoration, Poisson noise is usually used, or a shaping filter [85] may be used in order to transform non-Gaussian noise into Gaussian. Recently, Niu et al [84] proposed the use of a mixture of Gaussian (MoG) noise for multisensor fusion problems.…”

Section: For Instance)mentioning

confidence: 99%

A survey of classical methods and new trends in pansharpening of multispectral images

Amro

Mateos

Vega

et al. 2011

EURASIP J. Adv. Signal Process.

226

166

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There exist a number of satellites on different earth observation platforms, which provide multispectral images together with a panchromatic image, that is, an image containing reflectance data representative of a wide range of bands and wavelengths. Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image while simultaneously preserving its spectral information. In this paper, we provide a review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics. Finally, we analyze how the quality of the pansharpened images can be assessed both visually and quantitatively and examine the different quality measures proposed for that purpose.

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Section: For Instance)mentioning

confidence: 99%

A survey of classical methods and new trends in pansharpening of multispectral images

Amro

Mateos

Vega

et al. 2011

EURASIP J. Adv. Signal Process.

226

166

View full text Add to dashboard Cite

show abstract

“…PREDICTION AND OBSERVATION ERRORS Cross correlation problems were addressed by shaping filters, e.g., [3], [4]. In our case, the power spectral density of the colored noise corrupting the navigation velocity is difficult to correctly duplicate with any existent shaping filters.…”

Section: Analysis Of Cross Correlation Betweenmentioning

confidence: 99%

“…This cross correlation between the observation errors and system errors violate the assumption of the Kalman filter (KF) and could lead to filter divergence. Instead of using a tradition shaping filter ( [3], [4]) to address this cross correlation problem, a new method will be proposed in this paper by adding the cross correlation term into the KF. By running the filter with and without the cross-correlation term and comparing the error covariance of each individual state variable, a conclusion will be made as whether this term can be reasonably ignored... …”

Section: Introductionmentioning

confidence: 99%

Analysis of cross correlation between prediction and observation errors of an inertial navigation system

Liu

Adams

Liu

2009

2009 IEEE International Conference on Technologies for Practical Robot Applications

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Abstract-This paper investigates an approach to quantify the problem of cross correlation between the prediction and observation noise of an inertial navigation system (INS), which utilizes a linear Kalman filter (KF). Cross correlation is shown being introduced by use of the transformation matrix to transform body frame velocity observations into navigation frame. The effect of the cross-correlation term on the error covariance matrix and subsequently on the convergence of the filter is evaluated theoretically. With the cross-correlation term being formulated from the prediction and observation noise, it is incorporated into the KF and thus the relevant filter equations have been updated accordingly. The theoretical formulation and numerical simulations present the importance of incorporating this term into the filter. If this term was ignored, the error covariance estimates associated with the positional estimates would be too small and the filter would be "over confident".

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“…Obviously, the above projection introduces the cross-problem from process noise to measurement noise, which challenges the assumption in standard Kalman filter about the independence of process and measurement noise and decreased the accuracy of SINS/DVL. Cross-noise (or color noise) problem is a classical issue in data fusion and a large amount of paper published since the 1960s to deal with this problem [8][9][10][11]. But to the specific topic about SINS/DVL, the research is insufficient.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter for Cross-Noise in the Integration of SINS and DVL

Liu

et al. 2014

Mathematical Problems in Engineering

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The integration of strapdown inertial navigation system and Doppler velocity log (SINS/DVL) is widely used for navigation in automatic underwater vehicles (AUVs). In the integration of SINS/DVL, the velocity measured by DVL in body frame should be projected into navigation frame with the help of attitude matrix calculated by SINS to participate in data fusion. In the process of data fusion based on standard Kalman filter, the errors in calculated attitude matrix are characterized by state variance and process noise while the errors in measurement vector from DVL are by measurement noise. But the above projection will bring process noise into measurement noise, and thus the assumption of the independence between process noise and measurement noise will not stand. In this paper, the forming mechanism of cross-noise in SINS/DVL is studied in detail and Kalman filter for cross-noise is introduced to deal with this problem. Simulation results indicate that navigation accuracy, especially the position accuracy, can be improved when the cross-noise is processed in Kalman filter.

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Shaping filter representation of nonstationary colored noise

Cited by 14 publications

References 4 publications

A survey of classical methods and new trends in pansharpening of multispectral images

A survey of classical methods and new trends in pansharpening of multispectral images

Analysis of cross correlation between prediction and observation errors of an inertial navigation system

Kalman Filter for Cross-Noise in the Integration of SINS and DVL

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