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Radar Emitter Classification with Attribute-specific Recurrent Neural Networks
[article]
2019
arXiv
pre-print
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Radar pulse streams exhibit increasingly complex temporal patterns and can no longer rely on a purely value-based analysis of the pulse attributes for the purpose of emitter classification. ...
In this paper, we employ Recurrent Neural Networks (RNNs) to efficiently model and exploit the temporal dependencies present inside pulse streams. ...
Macro-averaged test accuracy for emitter classification networks with and without attribute-specific LSTMs along with different normalization schemes. • • • 0.1445 • 0.1449 • • • 0.5894 • 0.6006 • • • ...
arXiv:1911.07683v2
fatcat:od7wkyfjkrfyhdjtzjqkuhrxly
Artificial Intelligence Aided Electronic Warfare Systems-Recent Trends and Evolving Applications
2020
IEEE Access
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Recurrent neural network (RNN) and nonlinear autoregressive network with exogenous inputs (NARX) are examples of feedback ANN. ...
Another neural network based radar signal classification system is presented in [27] . ...
doi:10.1109/access.2020.3044453
fatcat:iuwn6w4uvbgdhlkiucg3sjadme
Radar Emitter Identification Based on Stacked Long and Short Term Memory
2021
DEStech Transactions on Materials Science and Engineering
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With the increasing complexity of electromagnetic environment and the rising of operating patterns of new radars, emitter identification is becoming more and more difficult. ...
The timing characteristics of the pulses are automatically extracted by SLSTM, and the optimal network parameters are trained to complete radar signal identification. ...
With the widely application of the deep learning techniques, the recurrent neural networks (RNNs) have shown an amazing ability in speech recognition, machine translation and other sequential data processing ...
doi:10.12783/dtmse/ameme2020/35532
fatcat:xta642cv7ndplcxisw67ob4d6y
Method for functional state recognition of multifunction radars based on recurrent neural networks
2021
IET radar, sonar & navigation
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Inspired by recent progress of deep neural networks, the authors propose to further develop radar signal modelling with recurrent neural networks. ...
For the multifunction radars (MFRs) with complex dynamical modes, the signal recognition needs to identify not only the emitter but also its current functional state. ...
There are many studies on radar signal recognition, which mainly focus on radar emitter classification (REC) and specific emitter identification (SEI) [3] [4] [5] . ...
doi:10.1049/rsn2.12075
fatcat:67jlgfyg7ndt7d67kgpwp4ahb4
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing
[article]
2020
arXiv
pre-print
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Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. ...
With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. ...
algorithm RNNs recurrent neural networks RS remote sensing RESISC remote sensing image scene classification RBF radial basis function REC radar emitter classification RBM restricted Boltzmann machine ...
arXiv:2009.13702v1
fatcat:m6am73324zdwba736sn3vmph3i
Attention-based Radar PRI Modulation Recognition with Recurrent Neural Networks
2020
IEEE Access
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INDEX TERMS Attention mechanism, electronic warfare, PRI modulation, recurrent neural network (RNN). ...
In this paper, we introduce an attention-based recognition framework based on recurrent neural network (RNN) to categorize pulse streams with complex PRI modulations and in environments with high ratios ...
A classification method based on convolutional neural network (CNN) is proposed in [21] . ...
doi:10.1109/access.2020.2982654
fatcat:kfxiuwkrg5cehl64fvumpho554
Online Non-cooperative Radar Emitter Classification from Evolving and Imbalanced Pulse Streams
2020
IEEE Sensors Journal
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Recent research treats radar emitter classification (REC) problems as typical closed-set classification problems, i.e., assuming all radar emitters are cooperative and their pulses can be pre-obtained ...
Specifically, a novel data stream clustering (DSC) algorithm, called dynamic improved exemplar-based subspace clustering (DI-ESC), is proposed, which consists of two phases, i.e., initialization and online ...
[1] , [13] , and even deep learning networks (e.g., convolutional neural networks (CNN) [14] and recurrent neural networks (RNN) [4] ) are introduced to classify the pulses based on the features. ...
doi:10.1109/jsen.2020.2981976
fatcat:qdtlu5khozcbrledyof7bjor2a
Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network and Deep Q-Learning Network
2020
IEEE Access
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Second, we design and pre-train a TFI feature extraction network for radar signals based on a convolutional neural network (CNN). ...
INDEX TERMS Radar signal recognition, Cohen class time-frequency distribution, convolutional neural network, deep Q-learning network. 49126 VOLUME 8, 2020 ...
MULTI-LABEL CLASSIFICATION NETWORK In this section, the fully connected layer and the Softmax layer of the neural network in the previous section are replaced by a recurrent neural network (RNN). ...
doi:10.1109/access.2020.2980363
fatcat:gfizn2w2yrad7kk75fpcq62cum
A unified method for deinterleaving and PRI modulation recognition of radar pulses based on deep neural networks
2021
IEEE Access
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In [22] , a method of estimating PRI using pulse width and PRI as inputs of the recurrent neural network was proposed, but this must be repeated continuously through re-input after extracting the pulse ...
PRI is a key characteristic of radar emitters and refers to the repetition period of the pulses transmitted by the radar. ...
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ ...
doi:10.1109/access.2021.3091309
fatcat:6fgodii7tncebk3d3c3co6wfki
Deep learning for aircraft classification from VHF radar signatures
2021
IET radar, sonar & navigation
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Encouraging initial results were obtained using convolutional or recurrent neural networks to classify aircraft classes, combining simulated bistatic RCS results and real trajectories (collected from automatic ...
This difference can be exploited to recognize the size of the aeroplanes with respect to these classes. ...
While both features appear insightful for aeroplane recognition, either convolutional neural networks (CNNs) or recurrent neural networks (RNNs) are proposed to deal with different representations of RCS ...
doi:10.1049/rsn2.12067
fatcat:f73wzbdlsbaehcp6yxme54f6kq
Blind Deinterleaving of Signals in Time Series with Self-attention Based Soft Min-cost Flow Learning
[article]
2020
arXiv
pre-print
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We then approximate the lower level optimization problem by self-attention based neural networks and provide a trainable framework that clusters the patterns in the input as the distinct flows. ...
We evaluate our method with extensive experiments on a large dataset with several challenging scenarios to show the efficiency. ...
Moreover, [12] utilizes recurrent neural networks (RNNs) to capture the current context of the pulse stream and predict attributes of the next PDWs. ...
arXiv:2010.12972v1
fatcat:mlzqazr2iffklavstfmd7ht634
Grand Challenges in Radar Signal Processing
2021
Frontiers in Signal Processing
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They also include automatic feature learning such as deep learning (e.g., deep belief networks (DBN), autoencoder (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative ...
In particular, in the scientific literature, we can find many applications of ML to solve problems related to the processing of radar signals, e.g., for the recognition and classification of radar emitters ...
doi:10.3389/frsip.2021.664232
fatcat:ekjgx65rhrgxdciw2zhb5bqmqq
Intra-pulse Modulation Radar Signal Recognition Based on CLDN Network
2019
IET radar, sonar & navigation
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In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals ...
Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. ...
Also, the small rectangle represents a layer of neural network with learnable parameters. ...
doi:10.1049/iet-rsn.2019.0436
fatcat:mwxovp65dfdtnjfxje7plojxni
Front Matter: Volume 10646
2018
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII
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These two-number sets start with 00, 01, 02, 03, 04, ...
Architectures to incorporate temporal processing in include Recurrent Neural Networks (RNN) and Long Short Term (memory) Networks (LSTM). ...
Neural Networks) From O. ...
doi:10.1117/12.2500434
fatcat:wfvvakrbsrfrbiiglzdvnp34o4
Table of Contents
2021
IEEE Transactions on Signal Processing
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White Balanced Neural Architecture Search and Its Application in Specific Emitter Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . (Contents Continued from Page xvii) D. Lahat, Y. ...
Vucetic RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection . . . . .K. Pratik, B. D. Rao, and M. ...
doi:10.1109/tsp.2021.3136800
fatcat:zhf46mb3rbdlnnh3u2xizgxof4
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