1971
DOI: 10.1109/tit.1971.1054585
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Shaping filter representation of nonstationary colored noise

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Cited by 14 publications
(7 citation statements)
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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%
“…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%
“…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%
“…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%