Non-Gaussian signal statistics in ocean SAR imagery

1998 ◽  
Vol 36 (2) ◽  
pp. 562-575 ◽  
Author(s):  
T. Eltoft ◽  
K.A. Hogda
Keyword(s):  
Gaussian Signal ◽  
Sar Imagery ◽  
Non Gaussian

Performance of the estimated ocean detector (EOD) against non‐Gaussian signal and noise

2006 ◽  
Vol 119 (5) ◽  
pp. 3426-3426
Author(s):  
Colin W. Jemmott ◽  
Jeffrey A. Ballard ◽  
Leon H. Sibul ◽  
R. Lee Culver ◽  
H. John Camin ◽  
...  
Keyword(s):  
Gaussian Signal ◽  
Non Gaussian

Nonlinear and non-Gaussian signal processing

2019 ◽  
pp. 299-312
Author(s):  
Max A. Little
Keyword(s):  
Machine Learning ◽  
Linear Time ◽  
Gaussian Model ◽  
Point Of View ◽  
Statistical Machine Learning ◽  
Statistical Point ◽  
Linear Time Invariant ◽  
Gaussian Signal ◽  
Non Gaussian ◽  
Time Invariant

Linear, time-invariant (LTI) Gaussian DSP, has substantial mathematical conveniences that make it valuable in practical DSP applications and machine learning. When the signal really is generated by such an LTI-Gaussian model then this kind of processing is optimal from a statistical point of view. However, there are substantial limitations to the use of these techniques when we cannot guarantee that the assumptions of linearity, time-invariance and Gaussianity hold. In particular, signals that exhibit jumps or significant non-Gaussian outliers cause substantial adverse effects such as Gibb's phenomena in LTI filter outputs, and nonstationary signals cannot be compactly represented in the Fourier domain. In practice, many real signals show such phenomena to a greater or lesser degree, so it is important to have a `toolkit' of DSP methods that are effective in many situations. This chapter is dedicated to exploring the use of the statistical machine learning concepts in DSP.

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