An adaptive noise-robust direction finding method based on SNR estimation

2014 ◽  
Author(s):  
Dongliang Guo ◽  
Zhonghua Li ◽  
Tiejun Zhang ◽  
Chao Huang
Keyword(s):  
Direction Finding ◽  
Snr Estimation ◽  
Adaptive Noise ◽  
Noise Robust ◽  
Finding Method

Use of Monopulse Difference-Phase Direction Finding Method for Location of Air Targets in Active-Passive Systems with Noise-Like Signal

2005 ◽  
Vol 63 (10) ◽  
pp. 863-869 ◽  
Author(s):  
Ye. N. Belov ◽  
Ye. M. Zarichnyak ◽  
V. I. Lutsenko ◽  
I. V. Lutsenko ◽  
V. G. Yakovlev
Keyword(s):  
Direction Finding ◽  
Passive Systems ◽  
Phase Direction ◽  
Finding Method

A High Accuracy Direction-Finding Method of 2-D Multi-Beams

2012 ◽  
Vol 229-231 ◽  
pp. 1577-1581
Author(s):  
Zhi Gang Wang ◽  
Fang Wang
Keyword(s):  
Direction Finding ◽  
Surface Fitting ◽  
High Accuracy ◽  
Simulation Experiments ◽  
Factors Influencing ◽  
Main Factors ◽  
Finding Method ◽  
Specific Influence

In order to acquire a kind of high accuracy multi-beams direction-finding method, this paper proposes a sort of 2-D multi-beams direction-finding method based on surface fitting. Three main factors influencing direction-finding accuracy are summarized in this paper, and the specific influence of these three factors to direction finding accuracy are analyzed by simulation experiments. The result of simulation experiments analysis shows that this method has higher steadiness and better direction-finding accuracy.

scholarly journals SBL-Based Direction Finding Method with Imperfect Array

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 426 ◽  
Author(s):  
Peng Chen  ◽  
Zhimin Chen ◽  
Xuan Zhang ◽  
Linxi Liu
Keyword(s):  
Mutual Coupling ◽  
Bayesian Learning ◽  
Linear Array ◽  
State Of The Art ◽  
Coupling Effect ◽  
Direction Finding ◽  
Noise Variance ◽  
Uniform Linear Array ◽  
Unknown Parameters ◽  
Finding Method

The imperfect array degrades the direction finding performance. In this paper, we investigate the direction finding problem in uniform linear array (ULA) system with unknown mutual coupling effect between antennas. By exploiting the target sparsity in the spatial domain, the sparse Bayesian learning (SBL)-based model is proposed and converts the direction finding problem into a sparse reconstruction problem. In the sparse-based model, the off-grid errors are introduced by discretizing the direction area into grids. Therefore, an off-grid SBL model with mutual coupling vector is proposed to overcome both the mutual coupling and the off-grid effect. With the distribution assumptions of unknown parameters including the noise variance, the off-grid vector, the received signals and the mutual coupling vector, a novel direction finding method based on SBL with unknown mutual coupling effect named DFSMC is proposed, where an expectation-maximum (EM)-based step is adopted by deriving the estimation expressions for all the unknown parameters theoretically. Simulation results show that the proposed DFSMC method can outperform state-of-the-art direction finding methods significantly in the array system with unknown mutual coupling effect.

scholarly journals Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kai Huang ◽  
Ming-Yi You ◽  
Yun-Xia Ye ◽  
Bin Jiang ◽  
An-Nan Lu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Direction Of Arrival ◽  
Direction Finding ◽  
Training Data ◽  
Data Driven ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Model Training ◽  
Finding Method

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

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