Results 11 to 20 of about 9,691 (222)

Two-Stage Fast DOA Estimation Based on Directional Antennas in Conformal Uniform Circular Array

open access: yesSensors, 2021
In conformal array radar, due to the directivity of antennas, the responses of the echo signals between different antennas are distinct, and some antennas cannot even receive the target echo signal.
Yao Xie   +4 more
doaj   +1 more source

Robust and sparse M-estimation of DOA

open access: yesSignal Processing, 2023
A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss ...
Christoph F. Mecklenbräuker   +3 more
openaire   +3 more sources

Performance analysis of deep neural networks for direction of arrival estimation of multiple sources

open access: yesIET Signal Processing, 2023
Recently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational ...
Min Chen, Xingpeng Mao, Xiuhong Wang
doaj   +1 more source

Deep Unfolded Gridless DOA Estimation Networks Based on Atomic Norm Minimization

open access: yesRemote Sensing, 2022
Deep unfolded networks have recently been regarded as an essential way to direction of arrival (DOA) estimation due to the fast convergence speed and high interpretability. However, few consider gridless DOA estimation.
Hangui Zhu   +4 more
doaj   +1 more source

A novel DOA estimation method for an antenna array under strong interference

open access: yesEURASIP Journal on Advances in Signal Processing, 2022
Strong interference will affect direction of arrival (DOA) estimation of weak desired signal and even cause DOA estimation failure. This paper investigates the weak signal DOA estimation for an antenna array under strong interference signals, and ...
Ming Zuo, Shuguo Xie
doaj   +1 more source

Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array

open access: yesSensors, 2023
Acoustic vector sensor (AVS) is a kind of sensor widely used in underwater detection. Traditional methods use the covariance matrix of the received signal to estimate the direction-of-arrival (DOA), which not only loses the timing structure of the signal
Yangyang Xie, Biao Wang
doaj   +1 more source

Gridless DOA Estimation With Multiple Frequencies

open access: yesIEEE Transactions on Signal Processing, 2023
Direction-of-arrival (DOA) estimation is widely applied in acoustic source localization. A multi-frequency model is suitable for characterizing the broadband structure in acoustic signals. In this paper, the continuous (gridless) DOA estimation problem with multiple frequencies is considered.
Yifan Wu 0015   +2 more
openaire   +4 more sources

Thinned coprime arrays for DOA estimation [PDF]

open access: yes2017 25th European Signal Processing Conference (EUSIPCO), 2017
Publication in the conference proceedings of EUSIPCO, Kos island, Greece ...
Ahsan Raza, Wei Liu 0001, Qing Shen 0002
openaire   +3 more sources

Sparse Convolutional Array for DOA Estimation

open access: yesEURASIP Journal on Advances in Signal Processing, 2022
Abstract Array signal processing plays an important role in many areas. Besides the Uniform Linear Array, their are many sparse array that have been proposed, for example, Minimum-redundancy array, Co-prime array, nested array, etc. However, most of the array structures have certain disadvantages.
Zikai Wang 0008   +4 more
openaire   +2 more sources

Toeplitz rectification and DOA estimation with music [PDF]

open access: yes2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
The MUSIC method is widely used in the field of DoA estimation using an array of M sensors, and is known to perform well as long as the number of available samples N is much larger than M. Nevertheless, in the scenario where N is of the same order of magnitude than M, its performance degrades, essentially because the sample covariance matrix (SCM) is ...
Vallet, Pascal, Loubaton, Philippe
openaire   +3 more sources

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