Jamming recognition method based on the polarisation scattering characteristics of chaff clouds

Liu, Yemin; Xing, Shu; Li, Yongzhen; Hou, Dong Yun; Wang, Xuesong

doi:10.1049/iet-rsn.2017.0121

Cited by 32 publications

(17 citation statements)

References 25 publications

(63 reference statements)

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“…k Á ε. U k _ t -the amplitude of the transmitting signal U k _ r -the amplitude of the echo signal σ k -the RCS of the kth chaff dipole R k -the distance between the kth chaff dipole and the radar system According to the induction scattering theory, the effective RCS σ of a chaff dipole can be expressed as [3]:…”

Section: Rðt 1 þmentioning

confidence: 99%

“…Releasing chaff cloud by chaff bombs is an effective deception interference measure for certain types of radar systems. Therefore, the technique of classifying chaff cloud and targets (such as missiles and aircrafts) for radar systems faces critical challenge [1][2][3]. It is well known that radar targets usually have micromotion characteristics that can be used to classify different targets [4].…”

Section: Introductionmentioning

confidence: 99%

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Research on the micromotion characteristics of a chaff cloud

Hao

2019

EURASIP J. Adv. Signal Process.

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This paper reports the investigation on the micromotion characteristics of chaff dipoles and a chaff cloud. Micromotion models of chaff dipoles and steady-state chaff clouds are discussed. Fourier transform method and time-frequency method are used to study the micromotion features of chaff dipoles, in the energy domain and time-frequency domain, respectively. Based on the characteristic parameters in these two domains, the simulation results show that there are micro-Doppler frequency components in the echo signals from chaff dipoles, and these frequency components vary periodically. Then, the micro-Doppler frequencies of chaff cloud are obtained based on the method of multipoint synthesis. Finally, the experimental results of chaff dipoles dispersion verify the micromotion characteristics of chaff dipoles and chaff cloud obtained by using the empirical mode decomposition method and the short-time Fourier transform method.

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Section: Rðt 1 þmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the micromotion characteristics of a chaff cloud

Hao

2019

EURASIP J. Adv. Signal Process.

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“…Then, typical radar jamming signals simulated by experts were used to evaluate the performance of the proposed method. There were 12 typical types of radar jamming signals, including suppression jamming such as aiming jamming, blocking jamming, sweeping jamming and ISRJ, distance deception jamming, dense false target jamming, smart noise jamming and typical passive jamming such as chaff jamming [14]. Finally, it also included the additive compound jamming such as ISRJ + chaff, dense false target + smart noise, and distance deception + sweeping.…”

Section: A Data Descriptionmentioning

confidence: 99%

“…Ma et al studied statistical algorithms to extract deception jamming features [13]. Liu et al [14] studied the polarization scattering characteristics of chaff jamming and classified them by support vector machines (SVM). However, the feature-based methods mainly rely on artificial feature extraction, and the process of artificial feature extraction has high computational complexity and requires a lot of manpower.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Radar Jamming Signal Classification With Sufficient and Limited Samples

2020

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Jamming is a big threat to radar system survival and anti-jamming is a part of the solution. The classification of radar jamming signal is the first step toward to anti-jamming. Recently, as an important part of deep learning, convolutional neural network (CNN) based methods have shown their capability in discriminant feature extraction and accurate classification. In this study, in order to harness the powerfulness of deep learning, CNN based methods are proposed to classify radar jamming signal acting on pulse compression radar. Specifically, a 1D-CNN is designed for radar jamming signal classification under the condition of sufficient training samples. Furthermore, due to the fact that the collection of sufficient training samples is time-consuming and expensive, a CNN-based siamese network is proposed for radar jamming signal classification to deal with the issue of limited training samples. The experimental results with sufficient and limited training samples show that the CNN-based classification methods obtain good classification performance in terms of classification accuracy and show a huge potential for radar jamming signal classification. INDEX TERMS Radar jamming signal, convolutional neural network (CNN), sufficient and limited training samples, siamese network.

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“…Micro-doppler signatures were utilized to discriminate moving target in [16]- [19]. The polarization scattering characteristics of chaff clouds were studied and a support vector machine (SVM) classification method was presented in [20]. In [21], the authors proposed a linear polarisation images based method for target recognition in passive millimeter-wave imaging system.…”

Section: Introductionmentioning

confidence: 99%

Using Polarization-Diverse Wave-Coefficients for Aerospace Target Recognition

et al. 2020

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Wave-coefficients (WCs) based target recognition can be theoretically applied to any a frequency range, with increased sensitivity to aspect angle at higher frequencies. When it is utilized to a frequency range in resonance region, the WCs can tolerate fairly big aspect variation and contain enough target information, bringing a competitive advantage in multitudinous target features. Four polarization channel WCs are introduced and independently utilized to identify four aerospace targets under noise and noise-free circumstances. To take full advantage of the WCs in different polarization channels, a novel radar aerospace target recognition method based on multi-polarization channel WCs is proposed in this paper. The majority vote rule and a maximum discrepancy rule are combined to identify a target when four polarization channel WCs are available at the same time. Simulation results on four aircraft models show that the proposed technique achieves a recognition performance at least comparable to the best single channel performance.

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Jamming recognition method based on the polarisation scattering characteristics of chaff clouds

Cited by 32 publications

References 25 publications

Research on the micromotion characteristics of a chaff cloud

Research on the micromotion characteristics of a chaff cloud

Convolutional Neural Network-Based Radar Jamming Signal Classification With Sufficient and Limited Samples

Using Polarization-Diverse Wave-Coefficients for Aerospace Target Recognition

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