Adaptive Beamforming Based on Instrumental Variable in Colored Noise

2002 ◽  
Author(s):  
Jianxun Li ◽  
Zhongliang Jing
Keyword(s):  
Numerical Simulations ◽  
Conventional Method ◽  
Instrumental Variable ◽  
Model Transformation ◽  
Colored Noise ◽  
Adaptive Beamforming ◽  
Noise Field

A method for adaptive beamforming based on model transformation is presented to overcome beam distortion with colored noise in this paper. The colored noise field is changed into the white one through the instrumental variable, further, the conventional method can be used. The proposed method is feasible for adaptive beamforming in colored noise, and numerical simulations are carried out.

scholarly journals User–Parameter–Free Robust Adaptive Beamforming Algorithm for Vector–Sensor Arrayswithin the Hypercomplex Framework

2013 ◽  
Vol 64 (2) ◽  
pp. 100-105
Author(s):  
Xiaoming Gou ◽  
Zhiwen Liu ◽  
Jingyan Ma ◽  
Yougen Xu
Keyword(s):  
Numerical Simulations ◽  
Sensor Arrays ◽  
Optimal Solution ◽  
Adaptive Beamforming ◽  
Model Errors ◽  
Vector Sensor ◽  
Two Component ◽  
Maximal Correlation ◽  
Robust Adaptive ◽  
Beamforming Algorithm

The major flaw of the conventional diagonal loading (DL) method is that it is unclear to choose appropriate DL levels or user-parameters (UPs), though several remarkable contributions have been made to regularize model errors without UPs. An UP-free algorithm for two-component vector-sensor arrays, which is robust to steering vector errors, is considered. The algorithm is within the hypercomplex framework using quaternions, and the optimal solution is found at the maximal correlation between the quaternionic and complex outputs. The performance of the proposed beamformer is illustrated via numerical simulations and is compared with several other UP-free adaptive beamformers

Reduction of Additive Colored Noise Using Coupled Dynamics

2016 ◽  
Vol 26 (01) ◽  
pp. 1650005 ◽  
Author(s):  
Vivek Kohar ◽  
Behnam Kia ◽  
John F. Lindner ◽  
William L. Ditto
Keyword(s):  
Numerical Simulations ◽  
Coupling Strength ◽  
Colored Noise ◽  
Periodic Regime ◽  
Noise Levels ◽  
Uhlenbeck Process ◽  
Coupled Dynamics ◽  
Ornstein Uhlenbeck Process ◽  
Optimal Coupling

We study the effect of additive colored noise on the evolution of maps and demonstrate that the deviations caused by such noise can be reduced using coupled dynamics. We consider both Ornstein–Uhlenbeck process as well as [Formula: see text] noise in our numerical simulations. We observe that though the variance of deviations caused by noise depends on the correlations in the noise, under optimal coupling strength, it decreases by a factor equal to the number of coupled elements in the array as compared to the variance of deviations in a single isolated map. This reduction in noise levels occurs in chaotic as well as periodic regime of the maps. Lastly, we examine the effect of colored noise in chaos computing and find that coupling the chaos computing elements enhances the robustness of chaos computing.

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