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DOA Estimation Method Based on Improved Deep Convolutional Neural Network
2022
Sensors
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For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. ...
The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a ...
Based on this, a DOA estimation method by deep convolutional neural networks (DCNN) is proposed. ...
doi:10.3390/s22041305
pmid:35214207
pmcid:PMC8963012
fatcat:pmzzsuaulvaebcbl72mr2mgl2a
DoA and ToA Estimation Method of OFDM Signal Based on Cascaded Deep Neural Network
2021
International Conference on Indoor Positioning and Indoor Navigation
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This paper proposes the DoA and ToA estimation method of OFDM signal based on a cascaded deep neural network (DNN) with a uniform grid array (UGA). ...
Simulation results show that the trained deep neural network has better estimation accuracy under multipath propagation and noisy interference environment compared with the conventional DoA and ToA estimation ...
Although much research has been done, the joint estimation of TOA and DoA based on neural networks is lacking unattended. ...
dblp:conf/ipin/ZhengF0RDZS21
fatcat:c6pnh2z3kfffpn6uupaqwexaoa
Small Sample Coherent DOA Estimation Method Based on S2S Neural Network Meta Reinforcement Learning
2023
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Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. ...
The accurate estimation of coherent DOA under the condition of small samples based on meta-reinforcement learning (MRL) is realized. ...
estimation method of small sample S2S neural network based on meta−reinforcement learning is studied. ...
doi:10.3390/s23031546
pmid:36772585
pmcid:PMC9918895
fatcat:yf5c5mevfzg5fk3jm5ekx4qpzu
A Survey on Applications of Multi Layer Perceptron Neural Networks in DOA Estimation for Smart Antennas
2013
International Journal of Computer Applications
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The multi layer perceptron (MLP) based neural networks have demonstrated their capability of estimating DOAs very effectively even for correlated signals. ...
Neural networks are non linear and use simple mathematical operators. They map the non linear behavior of smart antennas and perform DOA estimations accurately with considerable time reduction. ...
The neural network based DOA estimation is based on the inverse mapping i.e. F : C M → R K . ...
doi:10.5120/14670-2998
fatcat:tdf6w6n2snfj3p237iqapirdaa
Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks
2021
International Journal of Antennas and Propagation
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Besides, although the proposed 2D DOA neural network can only process one source at a time, we adopt a simple strategy that enables the proposed method to estimate the 2D DOA of multiple sources in turn ...
This paper presents a novel efficient high-resolution two-dimensional direction-of-arrival (2D DOA) estimation method for uniform circular arrays (UCA) using convolutional neural networks. ...
In recent years, neural network-based methods have been extensively developed for improving the operation speed and adaptability of DOA estimation. ...
doi:10.1155/2021/5516798
fatcat:wpn5nms7framxg4kss3xfzn6fa
Multilayer Perceptron Scheme for Beamforming and Channel Estimation of Massive MIMO
2019
International journal of recent technology and engineering
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Massive MIMO being one of the core enabler of 5G network, estimation of channel characteristics in such network plays a vital role. ...
In our proposed scheme, as both beamforming and channel estimation are handled by deep neural network, along with achieving very much better accuracy, mean square error (MSE) and bit error rate (BER) performance ...
Two deep neural network, one for beamforming and other for DOA estimation is used here. ...
doi:10.35940/ijrte.b1089.0782s619
fatcat:oknvydbeevc2xfc2wg2i7bfsjm
Multiple Signal Direction of Arrival (DoA) Estimation for a Switched-beam System Using Neural Networks
2007
PIERS Online
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A new Direction of Arrival (DoA) estimation method based on Neural Networks (NNs) is presented. ...
Simulations of DoA estimation tests show accurate results even for a big set of simultaneously incident signals. ...
DOA ESTIMATION METHOD AND NEURAL NETWORK TRAINING The proposed DoA estimation method is based on the application of strict power control at the mobile stations and on the a priori knowledge of the number ...
doi:10.2529/piers070215034532
fatcat:6vjmkmebwvfu3ixwqa7tmz42z4
Direction of arrival estimation based on phase differences using neural fuzzy network
2000
IEEE Transactions on Antennas and Propagation
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A new high-resolution direction of arrival (DOA) estimation technique using a neural fuzzy network based on phase difference (PD) is proposed in this paper. ...
To attach these problems, neural networks have become popular for DOA estimation in recent years. ...
ESTIMATION OF DOA USING A NEURAL FUZZY NETWORK In this section, we shall introduce a neural fuzzy network and then propose a high-resolution DOA estimation scheme based on this network with the phase differences ...
doi:10.1109/8.876331
fatcat:xpf7pilp3rbsxkvf65pwv6bqxq
Deep Learning Approach in DOA Estimation: A Systematic Literature Review
2021
Mobile Information Systems
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This study provides a systematic review of research on DOA estimation using deep neural network methods. ...
Then, the DL technology used in DOA estimation is systematically analyzed, including the purpose of using DL in DOA estimation, various DL models (convolutional neural network, deep neural network, and ...
[45] proposed a DOA estimation model based on a recurrent neural network. With the help of Toeplitz matrix reconstruction, the model can estimate DOA for signals with unknown signal sources. ...
doi:10.1155/2021/6392875
fatcat:jtmyuje6zff5bnonpui5qc2vym
TB-NET: A Two-Branch Neural Network for Direction of Arrival Estimation under Model Imperfections
2022
Electronics
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Conventionally, networks are based singly on regression or classification and may lead to unstable training and limited resolution. ...
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. ...
Generally, these methods can be divided into those based on regression networks or classification networks. ...
doi:10.3390/electronics11020220
fatcat:cllwjsjqdvck3bavacht3ehuxe
Deep Neural Network for Estimation of Direction of Arrival with Antenna Array
2020
IEEE Access
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PROPOSED DEEP NEURAL NETWORKS-BASED DIRECTION OF ARRIVAL ESTIMATION SYSTEM DESIGN In this paper, a comprehensive study is conducted to carry out the DOA estimation based on the DNNs. ...
For the DOA estimation network, the dimension of the input layer was the same as the output layer size of the detection neural network because the signals processed by the detection neural network were ...
doi:10.1109/access.2020.3012582
fatcat:o5az4kl3brgljg6eqvv7t4y3yi
Direction of arrival and state of polarization estimation using Radial Basis Function Neural Network (RBFNN)
2008
2008 National Radio Science Conference
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A Neural Network architecture is applied to the problem of Direction of Arrival (DOA) and state of polarization estimation using a uniform circular cross and tri-crossed-dipoles antenna array. ...
The network is then capable of estimating DOA not included in the training set through generalization and the corresponding state of polarization. ...
The main advantages of the neural network methods are that they outperform conventional linear algebra based methods in both speed and accuracy. ...
doi:10.1109/nrsc.2008.4542314
fatcat:i7vg2nrefffulagfr2sn22zdby
DIRECTION OF ARRIVAL AND STATE OF POLARIZATION ESTIMATION USING RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN)
2008
Progress in Electromagnetics Research B
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A Neural Network architecture is applied to the problem of Direction of Arrival (DOA) and state of polarization estimation using a uniform circular cross and tri-crossed-dipoles antenna array. ...
The network is then capable of estimating DOA not included in the training set through generalization and the corresponding state of polarization. ...
The main advantages of the neural network methods are that they outperform conventional linear algebra based methods in both speed and accuracy. ...
doi:10.2528/pierb07111801
fatcat:lvxn7n3ijzcyzlfbiwtreaflxe
Two-dimensional DOA estimation via deep ensemble learning
2020
IEEE Access
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In terms of the accuracy, it outperforms the neural network-based 2D DOA estimation and achieves performance comparable to the MUSIC algorithm. ...
To achieve fast and accurate two-dimensional (2D) direction of arrival (DOA) estimation, a novel deep ensemble learning method is presented in this paper. ...
In [15] , the authors provide a more accurate 2D DOA estimation method by developing an RBFNN-based model combining real electromagnetic sources and a simulated-based neural network. ...
doi:10.1109/access.2020.3005221
fatcat:5euk6zalunfd7ay6uzkiuvhj5q
Deep-learning-aided Low-complexity DOA Estimators for Ultra-Massive MIMO Overlapped Receive Array
[article]
2023
arXiv
pre-print
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Due to the high complexity of the training network based on large-scale arrays, in the OSAP-CBAM-CNN method, the complex network is divided into several smaller networks based on the overlapped subarray ...
Massive multiple input multiple output(MIMO)-based fully-digital receive antenna arrays bring huge amount of complexity to both traditional direction of arrival(DOA) estimation algorithms and neural network ...
CONVENTIONAL ML ESTIMATOR AND AP ALGORITHM DOA estimation based on ML methods has very good asymptotic performance compared to the super-resolution algorithm based on subspace decomposition in the coherent ...
arXiv:2301.06101v1
fatcat:aypq4w2r45ac5env3q5ju7vbq4
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