DOA Robust Estimation of Echo Signals Based on Deep Learning Networks With Multiple Type Illuminators of Opportunity

Hu, Bo; Liu, Mingqian; Yi, Fei; Song, Hao; Jiang, Fan; Gong, Fengkui; Zhao, Nan

doi:10.1109/access.2020.2966653

Cited by 12 publications

(13 citation statements)

References 28 publications

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“…To verify the effectiveness of the proposed passive detection method and investigate and the influence of different parameters on the detection performance, a series of simulation experiments are conducted using MATLAB (9.5.0.944444 (R2018b), MathWorks Company, Natick, MA, USA). Simulation parameters are set according to [29,30]. GPS satellite, DVB-S satellite and INMARSAT satellite signals are considered in the simulation with Gaussian white noise.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target

et al. 2020

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Passive detection of a moving aerial target is critical for intelligent surveillance. Its implementation can use signals transmitted from satellites. Nowadays, various types of satellites co-exist which can be used for passive detection. As a result, a satellite signal receiver may receive signals from multiple heterogeneous satellites, causing difficult in echo signal detection. In this paper, a passive moving aerial target detection method leveraging signals from multiple heterogeneous satellites is proposed. In the proposed method, a plurality of direct wave signals is separated in a reference channel first. Then, an adaptive filter with normalized least-mean-square (NLMS) is adopted to suppress direct-path interference (DPI) and multi-path interference (MPI) in a surveillance channel. Next, the maximum values of the cross ambiguity function (CAF) and the fourth order cyclic cumulants cross ambiguity function (FOCCCAF) correspond into each separated direct wave signal and echo signal will be utilized as the detection statistic of each distributed sensor. Finally, final detection probabilities are calculated by decision fusion based on results from distributed sensors. To evaluate the performance of the proposed method, extensive simulation studies are conducted. The corresponding simulation results show that the proposed fusion detection method can significantly improve the reliability of moving aerial target detection using multiple heterogeneous satellites. Moveover, we also show that the proposed detection method is able to significantly improve the detection performance by using multiple collaborative heterogeneous satellites.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target

et al. 2020

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“…19,20 CNNs are also used for sound source DOA estimation. [21][22][23] In Chakrabarty and Habets, 21 the CNN-based method showed robustness to noise and small perturbations in microphone positions. Hu et al 22 propose supervised learning algorithm for DOA estimation combining CNN and long short-term memory (LSTM).…”

Section: Background Of the Study And Limitations Of Current Doa Estimation Methodsmentioning

confidence: 99%

“…In Chakrabarty and Habets, 21 the CNN‐based method showed robustness to noise and small perturbations in microphone positions. Hu et al 22 propose supervised learning algorithm for DOA estimation combining CNN and long short‐term memory (LSTM). In Li et al, 23 a novel and robust DOA estimation method is proposed for echo signals with multiple illuminators of opportunity.…”

Section: Introductionmentioning

confidence: 99%

“…The rapidly evolving field of CNN applications in network design, management, routing, and control has witnessed several excellent contributions 19,20 . CNNs are also used for sound source DOA estimation 21–23 . In Chakrabarty and Habets, 21 the CNN‐based method showed robustness to noise and small perturbations in microphone positions.…”

Section: Introductionmentioning

confidence: 99%

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Direction of arrival estimation in multipath environments using deep learning

Harkouss

2021

Int J Communication

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This article aims to present a novel direction of arrival (DOA) estimation strategy in multipath environments using deep learning. Eigen decompositionbased algorithms, such as multiple signal classification (MUSIC), have highresolution DOA estimation performance, but they fail to work in the case of coherent signals. These algorithms require extensive computation and are difficult to implement in real time. Neural networks (multilayer perceptron [MLP] and radial basis function neural network [RBFNN]) are also applied to DOA estimation problem, and they are found to be faster than the conventional techniques, but they fail to ensure the desired accuracy in multipath environments when the training data set contains a huge number of samples and the number of incident signals was unknown. To enhance the DOA estimation performance, an efficient convolutional neural network (CNN)-based smart antenna is proposed. This smart antenna is composed of a uniform linear array (ULA), an intelligent DOA estimator, and an efficient adaptive beamformer, and the space is decomposed into five space sectors. The intelligent DOA estimator contains six CNN networks. One network is used as a classifier to select one or more space sectors, and five networks are used to calculate the DOAs of unknown number of coherent or noncoherent incident signals that received in the selected space sectors. The simulation results demonstrate that the proposed DOA estimator enables reliable DOA estimation despite very challenging multipath environment, and the CNN substantially reduces the CPU time for the DOA estimation computations especially when the number of incident signals is large.

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“…In recent years, the continuous development of deep learning theory and technology has provided new ideas and strategies for DOA estimation [ 14 ]. Deep learning-based DOA estimation methods can be divided into two main research branches: the first is a supervised algorithm that learns the projection relationship between the inputting feature and DOA.…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation Method Based on Improved Deep Convolutional Neural Network

Zhao

Zhan

et al. 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 binary sequence in which “1” indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input.

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DOA Robust Estimation of Echo Signals Based on Deep Learning Networks With Multiple Type Illuminators of Opportunity

Cited by 12 publications

References 28 publications

Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target

Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target

Direction of arrival estimation in multipath environments using deep learning

DOA Estimation Method Based on Improved Deep Convolutional Neural Network

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