scholarly journals Joint Signal Parameter Estimation in Non–Gaussian Noise by the Method of Polynomial Maximization

2016 ◽  
Vol 67 (3) ◽  
pp. 217-221 ◽  
Author(s):  
Volodymyr Palahin ◽  
Jozef Juhár
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Parameters Estimation ◽  
Polynomial Algorithms ◽  
Signal Parameter ◽  
The Third ◽  
Nonlinear Processing ◽  
Joint Parameters ◽  
The Moment ◽  
Non Gaussian

Abstract This paper considers the adaptation of the method of polynomial maximization for synthesis of the polynomial algorithms of joint signal parameter estimation in non-Gaussian noise. It is shown that the nonlinear processing of samples, the moment and the cumulant description of random variables in the form of cumulant coefficients of the third and higher orders can decrease the variance of joint parameters estimation as compared with the well-known results.

scholarly journals Modified Cramér-Rao Bound for $M$-FSK Signal Parameter Estimation in Cauchy and Gaussian Noise

2019 ◽  
Vol 68 (10) ◽  
pp. 10283-10288
Author(s):  
Junlin Zhang ◽  
Nan Zhao ◽  
Mingqian Liu ◽  
Yunfei Chen ◽  
Hao Song ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Signal Parameter ◽  
Cramer Rao Bound

scholarly journals Errata: Estimation of non-Gaussian noise parameters in the wavelet domain using the moment-generating function

2012 ◽  
Vol 21 (3) ◽  
pp. 039802-1
Author(s):  
Jan Švihlík ◽  
Karel Fliegel ◽  
Jaromír Kukal ◽  
Eva Jerhotová ◽  
Petr Páta ◽  
...  
Keyword(s):  
Generating Function ◽  
Gaussian Noise ◽  
Moment Generating Function ◽  
Wavelet Domain ◽  
Noise Parameters ◽  
The Moment ◽  
Non Gaussian

The influence of non-Gaussian noise on the accuracy of parameter estimation

2017 ◽  
Vol 381 (4) ◽  
pp. 216-220 ◽  
Author(s):  
Xue-Lian Li ◽  
Jun-Gang Li ◽  
Yuan-Mei Wang
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Non Gaussian

A robust method for the identification of non-Gaussian autoregressive systems in colored Gaussian noise

2020 ◽  
Vol 42 (13) ◽  
pp. 2499-2506
Author(s):  
Adnan Al-Smadi
Keyword(s):  
Gaussian Noise ◽  
Full Rank ◽  
Parameters Estimation ◽  
Gaussian Sequence ◽  
Third Order ◽  
Novel Technique ◽  
Structured Matrix ◽  
Power Spectral ◽  
Non Gaussian ◽  
Sequence Simulation

This paper introduces a novel technique for parameter estimation of an autoregressive (AR) all-pole process under non-Gaussian noise environment using third order cumulants of the observed sequence. The proposed AR parameters estimation technique is based on formulating a particular structured matrix with entries of third order cumulants of the observed output sequence only. This matrix almost possesses a full rank structure. The observed sequence may be contaminated with additive Gaussian noise (white or colored), whose power spectral density is unknown. The system is driven by a zero-mean independent and identically distributed (i.i.d) non-Gaussian sequence. Simulation results confirm the good numerical conditioning of the algorithm and the improvement in performance with respect to well-known methods even when the observed signal is heavily contaminated with Gaussian noise.

scholarly journals Modeling of joint signal detection and parameter estimation on the background of non-Gaussian noise

2015 ◽  
Vol 14 (3) ◽  
pp. 87-94 ◽  
Author(s):  
Volodimir Palahin ◽  
◽  
Vitaliy Filipov ◽  
Serhii Leleko ◽  
Oleksandr Ivchenko ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Signal Detection ◽  
Gaussian Noise ◽  
Non Gaussian
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