Robust Direction of Arrival (DOA) Estimation Using RBF Neural Network in Impulsive Noise Environment

2005 ◽  
pp. 332-337
Author(s):  
Hong Tang ◽  
Tianshuang Qiu ◽  
Sen Li ◽  
Ying Guo ◽  
Wenrong Zhang
Keyword(s):  
Neural Network ◽  
Impulsive Noise ◽  
Rbf Neural Network ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Noise Environment

scholarly journals Direction-of-Arrival Estimation over Sea Surface from Radar Scattering Based on Convolutional Neural Network

2021 ◽  
Vol 13 (14) ◽  
pp. 2681
Author(s):  
Xiuyi Zhao ◽  
Ying Yang ◽  
Kun-Shan Chen
Keyword(s):  
Neural Network ◽  
Point Source ◽  
Convolutional Neural Network ◽  
Array Signal Processing ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sea Surface ◽  
Estimation Methods ◽  
Wind Speeds ◽  
Wide Range

Conventional direction-of-arrival (DOA) estimation methods are primarily used in point source scenarios and based on array signal processing. However, due to the local scattering caused by sea surface, signals observed from radar antenna cannot be regarded as a point source but rather as a spatially dispersed source. Besides, with the advantages of flexibility and comparably low cost, synthetic aperture radar (SAR) is the present and future trend of space-based systems. This paper proposes a novel DOA estimation approach for SAR systems using the simulated radar measurement of the sea surface at different operating frequencies and wind speeds. This article’s forward model is an advanced integral equation model (AIEM) to calculate the electromagnetic scattered from the sea surface. To solve the DOA estimation problem, we introduce a convolutional neural network (CNN) framework to estimate the transmitter’s incident angle and incident azimuth angle. Results demonstrate that the CNN can achieve a good performance in DOA estimation at a wide range of frequencies and sea wind speeds.

scholarly journals TB-NET: A Two-Branch Neural Network for Direction of Arrival Estimation under Model Imperfections

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 220
Author(s):  
Liyu Lin ◽  
Chaoran She ◽  
Yun Chen ◽  
Ziyu Guo ◽  
Xiaoyang Zeng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Cross Entropy ◽  
Data Driven ◽  
Estimation Accuracy ◽  
Model Based ◽  
Classification And Regression ◽  
Grid Based

For direction of arrival (DoA) estimation, the data-driven deep-learning method has an advantage over the model-based methods since it is more robust against model imperfections. Conventionally, networks are based singly on regression or classification and may lead to unstable training and limited resolution. Alternatively, this paper proposes a two-branch neural network (TB-Net) that combines classification and regression in parallel. The grid-based classification branch is optimized by binary cross-entropy (BCE) loss and provides a mask that indicates the existence of the DoAs at predefined grids. The regression branch refines the DoA estimates by predicting the deviations from the grids. At the output layer, the outputs of the two branches are combined to obtain final DoA estimates. To achieve a lightweight model, only convolutional layers are used in the proposed TB-Net. The simulation results demonstrated that compared with the model-based and existing deep-learning methods, the proposed method can achieve higher DoA estimation accuracy in the presence of model imperfections and only has a size of 1.8 MB.

scholarly journals A Novel Approach to 3D-DOA Estimation of Stationary EM Signals Using Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2761
Author(s):  
Dong Chen ◽  
Young Hoon Joo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Gaussian Noise ◽  
Monitoring Network ◽  
Doa Estimation ◽  
Covariance Matrices ◽  
Noise Environment ◽  
Infinity Norm ◽  
The Neural Network ◽  
Non Gaussian

This paper proposes a novel three-dimensional direction-of-arrival (3D-DOA) estimation method for electromagnetic (EM) signals using convolutional neural networks (CNN) in a Gaussian or non-Gaussian noise environment. First of all, in the presence of Gaussian noise, four output covariance matrices of the uniform triangular array (UTA) are normalized and then fed into four neural networks for 1D-DOA estimation with identical parameters in parallel; then four 1D-DOA estimations of the UTA can be obtained, and finally, the 3D-DOA estimation could be obtained through post-processing. Secondly, in the presence of non-Gaussian noise, the array output covariance matrices are normalized by the infinity-norm and then processed in Gaussian noise environment; the infinity-norm normalization could effectively suppress impulsive outliers and then provide appropriate input features for the neural network. In addition, the outputs of the neural network are controlled by a signal monitoring network to avoid misjudgments. Comprehensive simulations demonstrate that in Gaussian or non-Gaussian noise environment, the proposed method is superior and effective in computation speed and accuracy in 1D-DOA and 3D-DOA estimations, and the signal monitoring network could also effectively control the neural network outputs. Consequently, we can conclude that CNN has better generalization ability in DOA estimation.

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