scholarly journals Noise Variance Estimation Using Image Noise Cross-Correlation Model on SEM Images

Scanning ◽  
2012 ◽  
Vol 35 (3) ◽  
pp. 205-212 ◽  
Author(s):  
K. S. Sim ◽  
M. E. Nia ◽  
C. P. Tso
Keyword(s):  
Cross Correlation ◽  
Variance Estimation ◽  
Image Noise ◽  
Correlation Model ◽  
Noise Variance ◽  
Sem Images ◽  
Noise Cross Correlation

scholarly journals Estimation of Object Motion State Based on Adaptive Decorrelation Kalman Filtering

2019 ◽  
Vol 23 (4) ◽  
pp. 749-757
Author(s):  
Xinmei Wang ◽  
Leimin Wang ◽  
Longsheng Wei ◽  
Feng Liu ◽  
◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Cross Correlation ◽  
Measurement Noise ◽  
Feature Point ◽  
Noise Variance ◽  
Filtering Method ◽  
Motion State ◽  
New Process ◽  
Object Feature ◽  
Noise Cross Correlation

To estimate the motion state of object feature point in image space, an adaptive decorrelation Kalman filtering model is proposed in this paper. The model is based on the Kalman filtering method. A first-order Markov sequence model is used to describe the colored measurement noise. To eliminate the colored noise, the measurement equation is reconstructed and then a cross-correlation between the process noise and the newly measurement noise is established. To eliminate the noise cross-correlation, a reconstructed process equation is proposed. According to the new process and measurement equations, and the noise mathematical characteristics of the standard Kalman filtering method, the parameters involved in the new process equation can be acquired. Then the noise cross-correlation can be successfully eliminated, and a decorrelation Kalman filtering model can be obtained. At the same time, for obtaining a more accurate measurement noise variance, an adaptive recursive algorithm is proposed to update the measurement noise variance based on the correlation method. It overcomes the limitations of traditional correlation methods used for noise variance estimation, thus, a relatively accurate Kalman filtering model can be obtained. The simulation shows that the proposed method improves the estimation accuracy of the motion state of object feature point.

Maximum-Likelihood, Magnitude-Based, Amplitude and Noise Variance Estimation

2021 ◽  
pp. 1-1
Author(s):  
Ming-Wei Wu ◽  
Yan Jin ◽  
Yan Li ◽  
Tianyu Song ◽  
Pooi Yuen Kam
Keyword(s):  
Maximum Likelihood ◽  
Variance Estimation ◽  
Noise Variance
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