Radar emitter recognition based on the short time fourier transform and convolutional neural networks

Wang, Xuebao; Huang, Gan; Zhou, Zhiwen; Gao, Jun

doi:10.1109/cisp-bmei.2017.8302111

Cited by 28 publications

(24 citation statements)

References 7 publications

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“…Attempts to reduce the redundancy in time-frequency (TF) feature have become an important subject [1][2][3]. Towards this end, some works [4][5][6] treat the TF spectrum as images. Then, downsampling of both variables is applied, and the result is sent to the classifier for identification.…”

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

et al. 2020

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Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex frequency modulation signals compared with existing handcrafted features. Numerous results demonstrate that the synchrosqueezing TF feature can offer a better recognition accuracy, especially for the signals with intricate time-varying information. Moreover, a linear relevance propagation algorithm is employed to attest to the SST feature importance from the perspective of deep learning.

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Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

et al. 2020

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“…Li-Hua et al [10] and Verstraete et al [11] used the time-frequency diagram of mechanical vibration signals and a CNN to diagnose fault in vibration signals, where deep image features corresponding to the vibration signal were successfully extracted via CNN. Time-frequency diagrams calculated from STFT of various types of radar signals were used with a CNN to discriminate radar signals in another study [12,13]. The time-frequency diagram of ultrasonic signals of broken glass in glass containers can be used to identify glass fragments by CNN [14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Classification Method for Flutter Signals Based on the CNN and STFT

Duan

Zheng

Liu

2019

International Journal of Aerospace Engineering

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Necessary model calculation simplifications, uncertainty in actual wind tunnel test, and data acquisition system error altogether lead to error between a set of actual experimental results and a set of theoretical design results; wind tunnel test flutter data can be utilized to feedback this error. In this study, a signal processing method was established to use the structural response signals from an aeroelastic model to classify flutter signals via deep learning algorithm. This novel flutter signal processing and classification method works by combining a convolutional neural network (CNN) with time-frequency analysis. Flutter characteristics are revealed in both time and frequency domains, which are harmonic or divergent in the time series; the flutter model energy is singular and significantly increases in the frequency view, so the features of the time-frequency diagram can be extracted from the dataset-trained CNN model. As the foundation of the subsequent deep learning algorithm, the datasets are placed into a collection of time-frequency diagrams calculated by short-time Fourier transform (STFT) and labeled with two artificial states, flutter or no flutter, depending on the source of the signal measured from a wind tunnel test on the aeroelastic model. After preprocessing, a cross-validation schedule is implemented to update (and optimize) CNN parameters though the trained dataset. The trained models were compared against test datasets to validate their reliability and robustness. Our results indicate that the accuracy rate of test datasets reaches 90%. The trained models can effectively and automatically distinguish whether or not there is flutter in the measured signals.

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“…To the best of our knowledge, DL has been studied for in emitter waveform recognition a little, and its application is simple. References directly adopted a CNN as the feature extractor to achieve recognition. However, the CNN was not fully exploited and the scenario of multiple emitters was not considered.…”

Section: Introductionmentioning

confidence: 99%

Ensemble convolutional neural networks for automatic fusion recognition of multi‐platform radar emitters

Zhou¹,

Huang

Wang

2019

ETRI Journal

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Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

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Radar emitter recognition based on the short time fourier transform and convolutional neural networks

Cited by 28 publications

References 7 publications

Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

A Novel Classification Method for Flutter Signals Based on the CNN and STFT

Ensemble convolutional neural networks for automatic fusion recognition of multi‐platform radar emitters

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