Micro-motion parameters estimation in non-Gaussian noise via mutual correntropy

2017 ◽  
Author(s):  
Dingding Xiong ◽  
Guolong Cui ◽  
Shisheng Guo ◽  
Lifang Feng ◽  
Xiaobo Yang
Keyword(s):  
Gaussian Noise ◽  
Parameters Estimation ◽  
Micro Motion ◽  
Motion Parameters ◽  
Non Gaussian

Cramer-Rao bounds for micro-motion parameters estimation in coherent laser detection

2017 ◽  
Vol 46 (7) ◽  
pp. 706002
Author(s):  
郭力仁 Guo Liren ◽  
胡以华 Hu Yihua ◽  
王云鹏 Wang Yunpeng
Keyword(s):  
Parameters Estimation ◽  
Laser Detection ◽  
Micro Motion ◽  
Motion Parameters

Micro-motion Parameters Estimation Using Randomized Stepped Frequency Radar

2019 ◽  
Author(s):  
Chuncheng Zhao ◽  
Yimin Liu ◽  
Tianyao Huang ◽  
Lei Wang ◽  
Huaiying Tan
Keyword(s):  
Parameters Estimation ◽  
Micro Motion ◽  
Motion Parameters ◽  
Stepped Frequency

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.

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.

Micro-motion Parameters Estimation of Space Precession Targets

2011 ◽  
Vol 33 (10) ◽  
pp. 2427-2432 ◽  
Author(s):  
Yong-sheng Guan ◽  
Qun-sheng Zuo ◽  
Hong-wei Liu ◽  
Lan Du ◽  
Yan-bing Li
Keyword(s):  
Parameters Estimation ◽  
Micro Motion ◽  
Motion Parameters

A new method of micro-motion parameters estimation based on cyclic autocorrelation function

2012 ◽  
Vol 56 (10) ◽  
pp. 1-11 ◽  
Author(s):  
Jie Niu ◽  
KangLe Li ◽  
WeiDong Jiang ◽  
Xiang Li ◽  
GangYao Kuang ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Parameters Estimation ◽  
New Method ◽  
Micro Motion ◽  
Motion Parameters

STATEMENT OF THE PROBLEM ON SYNTHESIS OF OPTIMAL EVALUATION OF SIGNAL PARAMETERS IN NON-GAUSSIAN NOISE

2012 ◽  
Vol 71 (17) ◽  
pp. 1541-1555
Author(s):  
V. A. Baranov ◽  
S. V. Baranov ◽  
A. V. Nozdrachev ◽  
A. A. Rogov
Keyword(s):  
Gaussian Noise ◽  
Signal Parameters ◽  
Non Gaussian ◽  
Optimal Evaluation
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