Robust DOA Estimator Under Non-Gaussian Noise and Insufficient Sample Support

2020 ◽  
Vol 39 (9) ◽  
pp. 4730-4739
Author(s):  
Hongwang Zhang ◽  
Zhi Zheng
Keyword(s):  
Gaussian Noise ◽  
Sample Support ◽  
Non Gaussian

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

NONPARAMETRIC DECODING OF BLOCK CODES IN CHANNELS WITH NON-GAUSSIAN NOISE

2013 ◽  
Vol 72 (11) ◽  
pp. 1029-1038
Author(s):  
M. Yu. Konyshev ◽  
S. V. Shinakov ◽  
A. V. Pankratov ◽  
S. V. Baranov
Keyword(s):  
Gaussian Noise ◽  
Block Codes ◽  
Non Gaussian

High Order Filters for Estimation in Non-Gaussian Noise

1993 ◽  
Author(s):  
Stelios C. A. Thomopoulos ◽  
Thomas W. Hilands
Keyword(s):  
Gaussian Noise ◽  
High Order ◽  
Non Gaussian

Wavelet threshold algorithm analysis under non-Gaussian noise background

2013 ◽  
Vol 32 (9) ◽  
pp. 2445-2447
Author(s):  
Qing-hua LI ◽  
Dalabaev Senbai ◽  
Xin-jian QIU ◽  
Chang LIAO ◽  
Quan-fu SUN
Keyword(s):  
Gaussian Noise ◽  
Algorithm Analysis ◽  
Noise Background ◽  
Threshold Algorithm ◽  
Non Gaussian

The Theory of Detection in Incompletely Characterized Non-Gaussian Noise

1985 ◽  
Author(s):  
Steven Kay
Keyword(s):  
Gaussian Noise ◽  
Non Gaussian

Centered error entropy Kalman filter with application to satellite attitude determination

2021 ◽  
pp. 014233122110198
Author(s):  
Baojian Yang ◽  
Lu Cao ◽  
Dechao Ran ◽  
Bing Xiao
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
Attitude Determination ◽  
Complexity Analysis ◽  
Iteration Algorithm ◽  
Robust Algorithms ◽  
Convergence Conditions ◽  
Satellite Attitude ◽  
Heavy Tailed ◽  
Non Gaussian

Due to unavoidable factors, heavy-tailed noise appears in satellite attitude estimation. Traditional Kalman filter is prone to performance degradation and even filtering divergence when facing non-Gaussian noise. The existing robust algorithms have limited accuracy. To improve the attitude determination accuracy under non-Gaussian noise, we use the centered error entropy (CEE) criterion to derive a new filter named centered error entropy Kalman filter (CEEKF). CEEKF is formed by maximizing the CEE cost function. In the CEEKF algorithm, the prior state values are transmitted the same as the classical Kalman filter, and the posterior states are calculated by the fixed-point iteration method. The CEE EKF (CEE-EKF) algorithm is also derived to improve filtering accuracy in the case of the nonlinear system. We also give the convergence conditions of the iteration algorithm and the computational complexity analysis of CEEKF. The results of the two simulation examples validate the robustness of the algorithm we presented.

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