Active Deception Jamming Research Articles

Deception Velocity-Based Method to Discriminate Physical Targets and Active False Targets in a Multistatic Radar System

Due to the silent operation of the receiver station in a multistatic radar system, it is difficult for the jammer to generate the cooperative active deception target for the multistatic radar system. Making use of the spatial diversity property, a data level fusion method is proposed to counter the active deception jamming in this paper. According to the spatial correlation difference in physical target and active false target motion states, the deception velocity of the physical target, which is obtained by the radial velocity of each receiver, obeys the Gaussian distribution with zero mean, and the one of the active false target obeys the Gaussian distribution with the mean being its true deception velocity. Based on this fact, the active false target and physical target are discriminated by the deception velocity testing. The proposed deception velocity-based (DVB) method can keep a constant misjudgment probability for physical targets and discriminate active false targets effectively, especially in large deception velocity cases. The simulation verifies the feasibility and validity of the proposed discrimination method. Moreover, the proposed method can be combined with the location information association method to enhance the ability to discriminate the range–velocity joint deception of false targets.

PSPNet: Pretraining and Self-Supervised Fine-Tuning-Based Prototypical Network for Radar Active Deception Jamming Recognition With Few Shots

PSPNet: Pretraining and Self-Supervised Fine-Tuning-Based Prototypical Network for Radar Active Deception Jamming Recognition With Few Shots

A method for active deception jamming discrimination based on frequency response

There are many types of radar active deception false target jamming that are highly correlated with the real target. Recognizing the real and false targets under a low Signal-to-Noise Ratio (SNR) is difficult. To solve the above problem, this article proposes a real/false target recognition method based on the features of multi-pulse joint frequency response by analyzing the differences in the scattering characteristics and modeling real target echoes as a synthesis of multi-scattering center echoes. Firstly, in the range-doppler domain, the real and false targets are truncated along the range dimension, and a fast Fourier transform is performed to extract the features of multi-pulse joint frequency response. Then, a two-channel feature fusion network is designed for real and false target recognition. Finally, a Multi-Coherent Processing Interval Joint Decision Method (M−CPIJDM) based on temporal information is proposed to improve the recognition performance. Experiments using the measured data show that the proposed method can well recognize real and false target signals under four jamming backgrounds: distance false target, velocity false target, distance-velocity composite false target, and forwarding dense false target.

Radar active deception jamming recognition based on Siamese squeeze wavelet attention network

AbstractActive deception jamming recognition has gained significant attention as a crucial aspect of modern electronic warfare, and a large quantity of jamming recognition methods based on either artificial or deep learning methods have been proposed to date. In actual complex battlefields, the abundant deceptive jamming signals are extremely difficult to obtain and the deceptive jamming signals leveraged by the adversarial jammers are often significantly influenced by noise. To address these challenges a Siamese squeeze wavelet attention network (SSWAN) for radar active deception jamming recognition method is proposed. By constructing a wavelet attention module (WAM), the fine‐grained time‐frequency texture features of echo and jamming signals are preferably extracted even under prohibitively strong noise scenarios. Specifically, the Siamese structure is employed as the main backbone network to measure the similarity between jamming signals and make full use of the training samples; besides, the squeeze learning module is embedded to maintain lightweight and prevent overfitting. Experimental results demonstrate that at a jamming‐to‐noise ratio (JNR) of −8 dB, the proposed method achieves recognition accuracies above 96.3% for 6‐class and multi‐class combinational active deceptive jamming types. Overall, compared to mainstream deep networks, the proposed method exhibits superior advantages in lower JNR ratio and small samples of actual battlefield scenarios.

Random-Frequency-Coded Waveform Optimization and Signal Coherent Accumulation Against Compound Deception Jamming

Among the various types of jamming, active deception jamming has similar characteristics to the transmitted signal. The jamming seriously affects the detection and tracking of real targets by radar. In recent years, waveform design has become an effective anti-jamming method with the maturity of waveform generator technology. However, most anti-jamming waveforms are only designed to combat a single kind of jamming. To compensate for the shortage of existing waveforms that cannot simultaneously combat compound deception jamming, we construct an interpulse-intrapulse joint frequency coded(I-IJFC) anti-jamming waveform and analyze the anti-jamming process flow. Moreover, to reduce the influence of residual jamming, we propose an intrapulse frequency-coded method based on jamming parameters estimation. The waveform can be used against cross-pulse repeater deception jamming(CRDJ) and interrupted sampling jamming(ISJ). Then, to solve the problem that the existing algorithm cannot realize the coherent accumulation of the proposed waveform, we propose a coherent accumulation algorithm of subpulse rearrangement compensation and nonuniform discrete Fourier transform (SRCNDFT). The simulation examples show that the waveform proposed in this paper performs better against compound deception jamming than existing waveforms, and the proposed algorithm can realize the coherent accumulation of I-IJFC echo signals.

A Periodic Multiple Phases Modulation Active Deception Jamming for Multistatic Radar System

Aiming at the problem that the amplitudes of false targets (FTs) are correlated among receivers in multistatic radar system (MSRS), a multi-jammer cooperative deception jamming method based on periodic multiple phases modulation (PMPM) is proposed. A FT string with random fluctuations in amplitude can be generated after the signal undergoes PMPM. In the distributed jammer system, each jammer adopts PMPM jamming method with different parameters, and then jointly transmit beamforming to forward jamming signals. Finally, the amplitude of the FTs between receivers is jointly affected by the azimuth angle and the phase modulation coefficient, which destroys the amplitude "coherence" of the FTs between receivers, and eliminates the amplitude symmetry and high-order attenuation characteristics of the FT strings generated by traditional jamming method. We study the amplitude compensation and amplitude correlation performance of the proposed jamming method, derive the rank of the ratio matrix of amplitude correlation factors, and prove that FTs have dispersiveness in the amplitude ratio space. In order to obtain the optimal and stable jamming effect, a genetic algorithm (GA)-based jamming modulation parameter optimization method is designed, which effectively drops the discrimination probability of physical targets (PTs) to 14% in the benchmark scenario. Simulation experiments demonstrate the feasibility and effectiveness of the jamming method.

Recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time–frequency diagrams as the input, which time–frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy.

Active Deception Jamming Recognition in the Presence of Extended Target

Accurate sensing of the main-lobe active deception jamming is critical for radar anti-jamming and extended target detection in complex electromagnetic environment. This letter therefore deals with the problem of multiple active deception jamming recognition in extended target settings. A residual convolutional neural network with attention mechanism-based radar active deception jamming recognition algorithm is proposed leveraging a hybrid model to capture much rich features through multi-domain feature fusion. The proposed method can outperform state-of-the-art methods in terms of recognition accuracy, model size, and convergence speed. Experimental results demonstrate its effectiveness and robustness.

Radar Deception Jamming Recognition Based on Weighted Ensemble CNN With Transfer Learning

With the development of new active deception jamming, radar antijamming has become a major research hotspot, and the recognition of jamming type is one of its key steps. In recent years, deep learning has been successfully applied in the field of radar jamming recognition, such as convolutional neural networks (CNNs). However, it is difficult to effectively improve the accuracy of deep learning algorithms in the case of small sample. Furthermore, ensemble learning and transfer learning can effectively improve the model generalization performance. For the small sample problem, this article proposes a weighted ensemble CNN with transfer learning (WECNN-TL)-based radar active deception jamming recognition algorithm. The main idea of this method is to obtain the time–frequency distribution maps of jamming signals by the short-time Fourier transform (STFT), and then, their real parts, imaginary parts, moduli, and phases are combined differently to construct multiple datasets. Finally, an ensemble CNN (ECNN) model with weighted voting and transfer learning is constructed to realize jamming recognition. Experiments on the simulated and measured mixed datasets (including 12 types of samples) show that the proposed method can get better recognition performance than random forest (RF), support vector machine (SVM), and some CNN-based methods.

Characteristic Parameter Optimization of Wireless Flaw Sensing System Based on Active Interference Suppression Algorithm

Based on the active interference suppression algorithm, this study combines the radar working mode and the interference type and realizes the effective detection of the flaw detection signal by successively processing the radar receiving signal and the filtering processing. Firstly, this article builds a simulation platform similar to the actual situation to verify the existing conventional active interference suppression algorithms. Secondly, for the detection of chirp active deception jamming signals entering from the main lobe, a radar active deception jamming detection method based on the characteristic parameter matching of the harmonic components of active deception jamming signals is proposed. After that, the spectral characteristics of the harmonic components of the deception interference signal are analyzed, and the center frequency and the tuning frequency of the real target echo are obtained. Finally, by establishing a frequency modulation parameter library for possible interference harmonic signal components, the acquisition phase of the radar gate by the jammer matched analysis with the preestablished frequency modulation parameter library is implemented to achieve active deception interference detection. This method can effectively detect active deception jamming signals in a complex tunnel environment. The interference suppression algorithms verified by simulation include noise FM interference suppression algorithm based on cancellation and distance false target interference suppression algorithm based on LFM radar summary processing. Through actual measurement data processing and analysis, the effectiveness of the method is verified and the idea of interference suppression is expanded. The construction of the simulation platform is obtained by appropriately modifying the actual parameters, a certain type of suppression jammer, and a certain type of deception jammer used in a certain countermeasure field test at a radar station.

Adaptive Dual-Polarization Filtering Method for Countering Active Jamming

In order to counter active jamming, an adaptive polarization filtering method based on dual polarization radar is put forward. First, the signal flow principle of the dual polarization radar and its signal model are introduced. Then, the weighted coefficient matrices of the polarization filter are calculated adaptively according to the actual work situation of the current radar. Finally, the specific polarization filtering algorithm and the output criterion of the optimal filtering results are given. Experimental results show that this method does not need to know the type, quantity, combination mode, polarization characteristics, and other prior knowledge of active jamming but has well effect on both active deception jamming and active blanket jamming, so it has strong engineering application value.

Hermitian Distance-Based Method to Discriminate Physical Targets and Active False Targets in A Distributed Multiple-Radar Architecture

In a distributed multiple-radar architecture, the spatial scattering properties of targets can be utilized to counter active deception jamming effectively. When the cooperative detection is performed, the difference in spatial scattering property makes the Hermitian distance of a false target considerably different from that of a physical target. The Hermitian distance of a false target increases with the raise of jamming-to-noise ratio, whereas the one of a physical target obeys an identical Beta distribution for all signal-to-noise ratios, which leads to the former much greater than the latter, especially in high jamming-to-noise ratio. Making full use of the difference, a novel method is proposed based on the Neyman-Pearson lemma to discriminate physical and active false targets in this paper. The proposed method overcomes the dependence of the available method on the prior information about the jamming environment. What's more, the proposed method can keep a constant misjudgement probability for physical targets and discriminate targets effectively in one pulse repetition interval. The theoretical analysis and simulation verify the feasibility and validity of the proposed discrimination method.

DOA Estimation Under Active Deception Jamming Environment

For the target DOA estimation under active deception jamming environment with limited samples, a novel DOA estimation method based on the combination of Adaptive Polarization Filter(APF) and Block Sparse Bayesian Learning(BSBL) algorithm is proposed. First, the interference energy is suppressed using APF. Then, the proposed method constructs a sparse Bayesian model under active deception jamming environment. The target DOA is estimated using the BSBL algorithm based on the neighbor time sampling correlation. Simulated and measured data processing results prove that the proposed method reduces the influence of interference on the BSBL algorithm, and has higher spatial resolution and higher angle measurement accuracy, comparing with the method based on the combination of APF and subspace-based DOA algorithms or maximum likelihood DOA algorithm.

Discrimination of Active False Targets Based on Hermitian Distance for Distributed Multiple-Radar Architectures

In a distributed multiple-radar architecture, the spatial scattering properties of targets can be utilized to counter active deception jamming effectively. When the cooperative detection is performed, the available method to discriminate active false targets needs to know the precise jammer location, and its performance will suffer significant deterioration with little location error. Aiming at the problem, we propose a novel method to discriminate false targets based on Hermitian distance. The difference in spatial scattering property leads to the difference in the Hermitian distances of different target combinations. The Hermitian distance between two false targets is much greater than that between two physical targets or that between a physical target and a false target, especially in high jamming-to-noise ratio. Based on the difference, hypothesis testing is performed to discriminate false targets. The proposed method does not require any prior information about the jamming environment and can discriminate the targets effectively in one pulse repetition interval. Moreover, the proposed method can restrict the upper bound of the misjudgment probability for physical targets. The theoretical analysis and simulation verify the feasibility and validity of the proposed discrimination method.

Countering method for active jamming based on dual-polarization radar seeker

In order to counter active jamming, an adaptive polarization cancellation method based on dual-polarization radar is proposed in the paper. First, respectively being obtained by vertical and horizontal polarization channels of dual-polarization radar, the signals from the real target and active jamming are analyzed in composition and characteristic. Then, the weight coefficient is timely derived in practical scene to adjust difference of amplitude and phase of active jamming signals respectively acquired by the two orthogonal polarization channels, so the method can completely eliminate active jamming, but just a little power change for the real target signal. Furthermore, a compensating method for the target signal is developed to improve the angle measuring accuracy of monopulse radars. And the influence factors of adaptive polarization cancellation are analyzed in detail by simulation. Finally, the experimental results prove that it is very effective on both active blanket jamming and active deception jamming.

A frequency‐domain three‐stage algorithm for active deception jamming against synthetic aperture radar

Efficient generation of jamming signal is an important but intractable issue in active deception jamming against synthetic aperture radar. Considerations must be given to both the computational complexity and the focus depth of the false scatterers of a deception template. However, existing methods cannot meet both demands mentioned above when generating jamming signal of an extended false scene or scattered false targets. In this study, a frequency-domain three-stage algorithm (FDTSA) is proposed. In theory, the jammer system is deliberately reformatted in the two-dimensional frequency domain. Accordingly, the implementation of the FDTSA can be effectively accelerated by fast Fourier transform and by separating the modulation process of a repeat jammer into three stages: the offline stage, the initialisation stage and the real-time modulation stage. Theoretical analyses and simulation results indicate that the FDTSA can get rid of severe focus deterioration of the false scatterers and reasonable computational load is required.

Analysis of Active Deception Jamming Model against SAR

The limitations of basic active deception jamming model against SAR reconnaissance were analyzed detailed, and the necessary conditions of false target deployment for the model were derived. Some modifications and adjustment of the basic model were made, for which target-centric signal transmission schedule was proposed instead of jammer-centric schedule, time-varying repetition delay and time-varying Doppler parameters of false signal were made instead fixed parameters. The modified model extended false target’s displacement and improved the fidelity of false target. And some calculation examples and discussions were done in the end.