Direction of Arrival (DoA) Estimation Under Array Sensor Failures Using a Minimal Resource Allocation Neural Network

2007 ◽  
Vol 55 (2) ◽  
pp. 334-343 ◽  
Author(s):  
S. Vigneshwaran ◽  
Narasimhan Sundararajan ◽  
P. Saratchandran
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Array Sensor ◽  
Sensor Failures ◽  
Minimal Resource

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 Direction-of-Arrival Estimation Under Array Sensor Failures with ULA

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 26445-26456 ◽  
Author(s):  
Bing Sun ◽  
Chenxi Wu ◽  
Junpeng Shi ◽  
Huai-Lin Ruan ◽  
Wen-Qiang Ye
Keyword(s):  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Array Sensor ◽  
Sensor Failures

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.

Neural network-aided sparse convex optimization algorithm for fast DOA estimation

2021 ◽  
pp. 014233122110490
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Liangtian Wan ◽  
Mengxing Huang
Keyword(s):  
Neural Network ◽  
Convex Optimization ◽  
Optimization Algorithm ◽  
Estimation Error ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Variable Number ◽  
Vector Model ◽  
Direction Of Arrival Estimation ◽  
Toeplitz Structure

In this paper, a fast sparse convex optimization algorithm based on a neural network is proposed to improve the direction of arrival estimation. First, a fast [Formula: see text]-sparse representation of the array covariance vector model based on the Hermitian Toeplitz structure of array covariance is established to reduce computational complexity in data dimension and variable number. Then, the estimation error upper bound problem is investigated, and a neural network-aided coefficient selection method is developed. The direction of arrival estimation problem is solved through spectral peak search. Finally, the algorithm is extended to the case of off-grid error. The algorithm’s advantages in accuracy, calculation speed and robustness is verified by the simulations.

Sign in / Sign up

Export Citation Format

Share Document