Radar emitters classification and clustering with a scale mixture of normal distributions

Revillon, Guillaume; Mohammad-Djafari, Ali; Enderli, Cyrille

doi:10.1049/iet-rsn.2018.5202

Cited by 22 publications

(5 citation statements)

References 46 publications

(43 reference statements)

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“…Taking the PDW sequences as Multivariate Time Series (MTS), unsupervised feature extraction and clustering methods can be investigated for a more general applied and less prior required solution. There are recent studies considering the time series clustering of radar signals [22,35]. Guillaume [35] focus on clustering pulse sequences from different radar emitters, and the mean value is used to represent the time series characteristics of a pulse sequence.…”

Section: Introductionmentioning

confidence: 99%

“…There are recent studies considering the time series clustering of radar signals [22,35]. Guillaume [35] focus on clustering pulse sequences from different radar emitters, and the mean value is used to represent the time series characteristics of a pulse sequence. Their method achieves satisfactory performance as the parameter values of different emitters are differentiable in high-dimensional PDW spaces.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Time Series Feature Extraction and Clustering Framework for Multi-Function Radar Work Mode Recognition

Fan,

Zhu,

Zhang

2024

Electronics

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Multi-Function Radars (MFRs) are sophisticated sensors with great agility and flexibility in adapting their transmitted waveform and control parameters. The recognition of MFR work modes based on the intercepted pulse sequences plays an important role in interpreting the functional purpose and threats of a non-cooperative MFRs. However, due to the increased flexibility of MFRs, radar work modes with emerging new modulations and control parameters always appear, and the supervised classification method suffers performance degradation or even failure. Unsupervised learning and clustering of MFR pulse sequences becomes urgent and important. This paper establishes a unified multivariate MFR time series feature extraction and clustering framework for MFR work mode recognition. At first, various features are collected to form the feature set. The feature set includes features extracted through deep learning based on recurrent auto-encoders, multidimensional time series toolkit features, and manually crafted features for radar inter-pulse modulations. Subsequently, several feature selection algorithms, combined with different clustering and classification methods, are used for the selection of an “optimal” feature subset. Finally, the effectiveness and superiority of the proposed framework and selected features are validated through simulated and measured datasets. In the simulated dataset containing 20 classes of work modes, under the most severe non-ideal conditions, we achieve a clustering purity of 73.46% and an NMI of 84.28%. In the measured dataset with seven classes of work modes, we achieve a clustering purity of 86.96% and an NMI of 90.10%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate Time Series Feature Extraction and Clustering Framework for Multi-Function Radar Work Mode Recognition

Fan,

Zhu,

Zhang

2024

Electronics

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“…Radar signal recognition, one of the key technologies of modern electronic intelligence systems, plays an important role in modem electronic warfare. Traditional radar signal recognition mainly uses statistical pattern recognition approaches to identify the type or individual of the emitter to which the target signal belongs and thus evaluate the potential threats, namely radar emitter classification (REC) and specific emitter identification (SEI) [1][2][3]. With the development of technology, multifunction radars (MFRs) have been widely deployed.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars

Pan

2022

Sensors

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With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not only the type or individual of the emitter but also its current state. Existing methods identify MFR states through hierarchical modeling, but most of them rely heavily on prior information. In the paper, we focus on the MFR state recognition with actual intercepted MFR signals and develop it by introducing recurrent neural networks (RNNs) of deep learning into the modeling of MFR signals. According to the layered MFR signal architecture, we propose a novel end-to-end state recognition approach with two RNNs’ connections. This approach makes full use of RNNs’ ability to directly tackle corrupted data and automatically learn the features from input data. So, it is practical and less dependent on prior information. In addition, the hierarchical modeling method applied to the end-to-end network effectively restricts the scale of the end-to-end model so that the model can be trained with a small amount of data. Simulation results on a real MFR show the excellent recognition performance of our end-to-end approach with little prior information.

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“…However, the increasing complexity of the electromagnetic environment makes the task of SEI more and more difficult. For example, a low signal-to-noise ratio (SNR) environment may increase measurement loss or error, resulting in poor performance [ 4 ]. In order to suppress the influence of noise and improve the recognition accuracy, extensive research has been performed, such as using compressed sensing reconstruction algorithms to recover structured signals [ 5 , 6 , 7 , 8 ], as well as using complex-valued neural networks to improve the anti-noise performance [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Zhao

Yang

Liu

et al. 2022

Entropy

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Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10–16% from 10–15 SNR.

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Radar emitters classification and clustering with a scale mixture of normal distributions

Cited by 22 publications

References 46 publications

Multivariate Time Series Feature Extraction and Clustering Framework for Multi-Function Radar Work Mode Recognition

Multivariate Time Series Feature Extraction and Clustering Framework for Multi-Function Radar Work Mode Recognition

An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

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