A nonparametric phase estimation method for SIMO systems based on second-order and higher order statistics

1999 ◽  
Vol 47 (3) ◽  
pp. 843-847 ◽  
Author(s):  
Zhen Mao ◽  
Zhi Ding
Keyword(s):  
Order Statistics ◽  
Estimation Method ◽  
Higher Order ◽  
Second Order ◽  
Phase Estimation ◽  
Higher Order Statistics

Propagating Skewness and Kurtosis Through Engineering Models for Low-Cost, Meaningful, Nondeterministic Design

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Travis V. Anderson ◽  
Christopher A. Mattson
Keyword(s):  
Order Statistics ◽  
Taylor Series ◽  
Computational Cost ◽  
Higher Order ◽  
Second Order ◽  
System Model ◽  
Higher Order Statistics ◽  
Actual System ◽  
System Output ◽  
Skewness And Kurtosis

System models help designers predict actual system output. Generally, variation in system inputs creates variation in system outputs. Designers often propagate variance through a system model by taking a derivative-based weighted sum of each input’s variance. This method is based on a Taylor-series expansion. Having an output mean and variance, designers typically assume the outputs are Gaussian. This paper demonstrates that outputs are rarely Gaussian for nonlinear functions, even with Gaussian inputs. This paper also presents a solution for system designers to more meaningfully describe the system output distribution. This solution consists of using equations derived from a second-order Taylor series that propagate skewness and kurtosis through a system model. If a second-order Taylor series is used to propagate variance, these higher-order statistics can also be propagated with minimal additional computational cost. These higher-order statistics allow the system designer to more accurately describe the distribution of possible outputs. The benefits of including higher-order statistics in error propagation are clearly illustrated in the example of a flat-rolling metalworking process used to manufacture metal plates.

Research of Time Delay Estimation Based on Higher Order Statistics

2014 ◽  
Vol 530-531 ◽  
pp. 550-553 ◽  
Author(s):  
Qing Liu ◽  
Shuang Wang Han ◽  
Xiao Shu Ma
Keyword(s):  
Time Delay ◽  
Order Statistics ◽  
Estimation Method ◽  
Higher Order ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Estimation Accuracy ◽  
Higher Order Statistics ◽  
Delay Test ◽  
Bispectrum Estimation

The time delay estimation (TDE) is one of the most important techniques of characterization signal parameter. Delay parameter estimation is active in the field of signal processing. In this paper, On the basis of the basic theory method of time delay, the correlated time delay estimation, mutual-cumulants estimation and bispectrum estimation are analysed and discussed. Theoretical analysis and experimental results show that three methods is effectively used in time delay test, but higher order statistics (HOS) method can achieve the best effect. The HOS estimation method has many advantages, such as time delay estimation accuracy, high relative error small for Gaussian noise elimination ability.

Evaluation of a new Power Quality index, based in Higher Order Statistics

2017 ◽  
Vol 1 (15) ◽  
pp. 37-42
Author(s):  
J.M. Sierra-Fernández ◽  
J.J. González De La Rosa ◽  
A. Agüera-Pérez ◽  
J.C. Palomares Salas ◽  
O. Florencias-Oliveros
Keyword(s):  
Order Statistics ◽  
Power Quality ◽  
Quality Index ◽  
Higher Order ◽  
Higher Order Statistics
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