Search Results (21)

In complex electromagnetic environments, spatial coupling uncertainties—position errors and timing jitter—increase false target information entropy, reducing strategy effectiveness and posing challenges for robust UAV swarm track deception. This paper proposes an error-constrained entropy-minimizing compensation framework to model radar/UAV errors and their spatial coupling. The framework establishes closed-form gate association conditions based on the principle of entropy minimization, ensuring mutual consistency of false target measurements across multiple radars. Two strategies are proposed to reduce false target information entropy: 1. Zonal track compensation forms dense “information entropy bands” around each preset false target by inserting auxiliary deception echoes, enhancing mutual information concentration in the measurement space; 2. Formation jamming compensation adaptively reshapes the UAV swarm into regular polygons, leveraging geometric symmetry to suppress spatial diffusion of position errors. Simulation results show that compared with traditional methods, the proposed approach reduces the spatial inconsistency entropy by 50%, improving false target consistency and radar deception reliability. Full article

In existing phased-array radar systems, anti-jamming strategies are mainly generated through manual judgment. However, manually designing or selecting anti-jamming decisions is often difficult and unreliable in complex jamming environments. Therefore, reinforcement learning is applied to anti-jamming decision-making to solve the above problems. However, the existing anti-jamming decision-making models based on reinforcement learning often suffer from problems such as low convergence speeds and low decision-making accuracy. In this paper, a multi-aspect improved deep Q-network (MAI-DQN) is proposed to improve the exploration policy, the network structure, and the training methods of the deep Q-network. In order to solve the problem of the $ϵ$-greedy strategy being highly dependent on hyperparameter settings, and the Q-value being overly influenced by the action in other deep Q-networks, this paper proposes a structure that combines a noisy network, a dueling network, and a double deep Q-network, which incorporates an adaptive exploration policy into the neural network and increases the influence of the state itself on the Q-value. These enhancements enable a highly adaptive exploration strategy and a high-performance network architecture, thereby improving the decision-making accuracy of the model. In order to calculate the target value more accurately during the training process and improve the stability of the parameter update, this paper proposes a training method that combines n-step learning, target soft update, variable learning rate, and gradient clipping. Moreover, a novel variable double-depth priority experience replay (VDDPER) method that more accurately simulates the storage and update mechanism of human memory is used in the MAI-DQN. The VDDPER improves the decision-making accuracy by dynamically adjusting the sample size based on different values of experience during training, enhancing exploration during the early stages of training, and placing greater emphasis on high-value experiences in the later stages. Enhancements to the training method improve the model’s convergence speed. Moreover, a reward function combining signal-level and data-level benefits is proposed to adapt to complex jamming environments, which ensures a high reward convergence speed with fewer computational resources. The findings of a simulation experiment show that the proposed phased-array radar anti-jamming decision-making method based on MAI-DQN can achieve a high convergence speed and high decision-making accuracy in environments where deceptive jamming and suppressive jamming coexist. Full article

With the rapid development of electronic technology, the electromagnetic interference encountered by airborne synthetic aperture radar (SAR) is no longer satisfied with a single type of interference, and it often encounters both suppressive and deceptive interference. In this manuscript, an algorithm based on blind signal separation (BSS) and deep residual learning is proposed for airborne SAR multi-electromagnetic interference suppression. Firstly, theoretical airborne SAR imaging in a multi-electromagnetic interference environment model is established, and the signal-mixed model of multi-electromagnetic interference is proposed. Then, a BSS algorithm using maximum kurtosis deconvolution and improved principal component analysis (PCA) is presented for suppressing the composite electromagnetic interference encountered by airborne SAR. Finally, in order to find the desired signal among multiple separated sources and to cope with the residual noise, a deep residual network is designed for signal recognition and denoising. This method uses a BSS algorithm with maximum kurtosis deconvolution and improved PCA to perform mixed signal separation. After performing signal separation, the original echo signal and the jamming can be obtained. To solve the separation order uncertainty and residual noise problems of the existing BSS algorithms, the deep residual network is designed to recognize airborne SAR signals after airborne SAR imaging. This algorithm has a better signal restoration degree, higher image restoration degree, and better compound interference suppression performance before and after anti-interference. Simulation and measurement results demonstrate the effectiveness of our presented algorithm. Full article

Radar jamming waveform design is a vital part of radar jamming. Over seven to eight decades of evolution, the field has transitioned from noise signal design to coherent jamming signal design, resulting in a multitude of complex jamming styles capable of achieving deceptive jamming, suppressive jamming, and smart noise jamming, which combines both deception and suppression. For the first time, this article establishes a general formula for unifying jamming waveform design. Building upon this foundation, we systematically categorize jamming techniques, and provide an in-depth summary of the corresponding principles and conduct comparative analysis of their effectiveness against different targets. Finally, we look forward in terms of future research directions in jamming waveform design, including quantifiable jamming effect evaluation indicators, combined jamming styles, and intelligent jamming waveform generation methods to address the continuous advancement of radar technology and the complexity and variability of the electromagnetic environment. In order to fill the gap in the literature by summarizing the current state of the field and highlighting key challenges and opportunities, this review provides a comprehensive overview of radar jamming waveform design, categorizes and compares different jamming techniques, and identifies future research directions. Full article

Jamming recognition is a significant prior step to achieving effective jamming suppression, and the precise results of the jamming recognition will be beneficial to anti-jamming decisions. However, as the electromagnetic environment becomes more complex, the received signals may contain both suppression jamming and deception jamming, which is more challenging for existing methods focused on a single kind of jamming. In this paper, a recognition method for compound jamming based on a dual-channel neural network and feature fusion is proposed. First, feature images of compound jamming are extracted by the short-time Fourier transform and the wavelet transform. Feature images are then employed as inputs for the proposed network. During parallel processing in dual-channel, the proposed network can adaptively extract and learn task-relevant features via the attention modules. Finally, the output features in dual-channel are fused in the fusion subnetwork. Compared with existing methods, the proposed method can yield better recognition performance with less inference time. Additionally, compared with existing fusion strategies, the fusion subnetwork can further improve the recognition performance under low jamming-to-noise ratio conditions. Results with the semi-measured datasets also verify the feasibility and generalization performance of the proposed method. Full article

Digital radio frequency memory (DRFM) has emerged as an advanced technique to achieve a range of jamming signals, due to its capability to intercept waveforms within a short time. multiple-input multiple-output (MIMO) radars can transmit agile orthogonal waveform sets for different pulses to combat DRFM-based jamming, where any two groups of waveform sets are also orthogonal. In this article, a group orthogonal waveform optimal design model is formulated in order to combat DRFM-based jamming by flexibly designing waveforms for MIMO radars. Aiming at balancing the intra- and intergroup orthogonal performances, the objective function is defined as the weighted sum of the intra- and intergroup orthogonal performance metrics. To solve the formulated model, in this article, a group orthogonal waveform design algorithm is proposed. Based on a primal-dual-type method and proper relaxations, the proposed algorithm transforms the original problem into a series of simple subproblems. Numerical results demonstrate that the obtained group orthogonal waveforms have the ability to flexibly suppress DRFM-based deceptive jamming, which is not achievable using p-majorization–minimization (p-MM) and primal-dual, two of the most advanced orthogonal waveform design algorithms. Full article

Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed. Full article

Self-protection deceptive interferences (SPDI) are widely used in electronic countermeasures. Smeared spectrum (SMSP) interference, as a typical SPDI, can form a large number of dense false targets at the receiver output to affect effective target detection. Therefore, the suppression of SMSP interference is a compelling issue. The existing SMSP interference suppression methods inevitably result in energy loss of the target due to signal processing. This paper proposes a novel interference suppression method based on echo preprocessing to address this problem. Firstly, the pulse compression (PC) and the coherent integration (CI) characteristics of SMSP interference in the pulse Doppler radar are obtained through the derivation of formulas. Then, echo preprocessing is introduced, and the steps of interference suppression are listed in detail. Finally, the SMSP interference is suppressed because the preprocessed interference forms a center-shifting and range-scaling in the distance dimension after PC, and CI gain cannot be further obtained. The proposed method does not lose the energy of the true target because it does not involve filtering and reconstruction processing. Simulations show that the target detection probability of the proposed method can reach 100% via peak search after the interference suppression when the signal-to-noise ratio is greater than −10 dB and the jamming-to-signal ratio (JSR) is less than 35 dB. Compared with three representative methods in the recent literature, the proposed method has better robustness and higher JSR tolerance. Full article

Multiple input multiple output with frequency diversity array (FDA-MIMO) radar has unique advantages in mainlobe deception jamming suppression and target location. However, if the training sample contains the target signal, it will lead to poor jamming suppression performance and large target measurement error. To deal with the problem, a method of coarse target location in the time domain is proposed based on the cumulative sampling analysis. Taking full advantages of the strongest correlation characteristic between the expected steering vector and the true target, the feature vector and feature value corresponding to the true target are found after feature decomposition. The time domain location of the target is roughly estimated during the cumulative sampling analysis from near to far. Then, a pure jamming training sample can be obtained by avoiding the location. Noise subspace projection algorithm is used to measure the angle and range of the target while suppressing mainlobe jamming. The simulation results show that the proposed method can roughly estimate the target location in the time domain when the mainlobe deception jamming completely covers the target. Compared with conventional methods, the performance of jamming suppression and target localization error are closer to the performance of ideal sampling. Full article

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by comparing sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discontinuity of the interference signal in the time domain is proposed in this paper. The method proposed in this paper can recognize real and false targets in all ISRJ modes without any prior information, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3 dB loss of pulse compression gain, it can completely suppress different kinds of ISRJ interference when the SNR before pulse compression is higher than −20 dB, with 100% target detection probability. Full article

The Global Navigation Satellite System (GNSS) has been applied to all aspects of social livelihood and military applications and has become an important part of national infrastructure construction. However, due to the vulnerability of GNSS, satellite navigation jamming technology can pose a serious threat to GNSS security applications, and this has become a research hotspot in the field of navigation countermeasures. In this paper, satellite navigation jamming technologies are divided into suppression jamming and deception jamming, and the research status of satellite navigation suppression jamming and deception jamming technologies are sorted by three aspects: jamming technology classification, jamming efficiency evaluation, and jamming source deployment. Finally, the future development trend of satellite navigation jamming technology is summarized. Full article

Frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar makes it transmit beam range–angle two-dimensional freedom by attaching a small frequency offset increment between the array elements. With the widespread use of digital radio frequency memory (DRFM) technology, it is able to delay the false target to any range bin and doppler unit by delaying and forwarding the radar transmits signals, resulting in the deceptive jamming effect of range dimension. To this end, based on the research of the existing main-lobe deceptive interference methods, this paper proposes a new improved MUSIC method based on the FDA-MIMO radar against deceptive jamming in the range dimension. Firstly, the range information of target and jamming is determined through spatial spectrum search by using the mismatch of jamming in the airspace position and range bin position, and then the process of range dimension deceptive jamming suppression is given. The simulation analysis results show that the proposed anti-jamming method can effectively mitigate against range dimension deceptive jamming. Full article

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar solves the problem of angle pointing and range pointing during beam transmission due to its unique range-angle dimension dependent, which gives it good characteristics against main lobe deceptive jamming. Moreover, the current single range dimension deceptive jamming is easy to be identified and cannot form an effective jamming effect on FDA-MIMO radar. To address this problem, this paper proposes a compound jamming method of suppression and range deceptive against FDA-MIMO beamforming. Compared with the traditional deceptive jamming method, this method can reasonably utilize the beam gain, and minimum variance distortionless response (MVDR) adaptive beamforming cannot significantly reduce the power of the jamming, thus avoiding the jamming being weakened at the beamforming level, and the jamming may appear in different range bins, causing significant suppression and range deceptive compound jamming effects on FDA-MIMO radar in the range dimension. The simulation verified that the compound jamming method can effectively interfere with the FDA-MIMO radar at the beam-forming level, and the jamming effect is good. Full article

Jamming will seriously affect the detection ability of radar, so it is essential to suppress the jamming of radar echoes. Interrupted-sampling repeater jamming (ISRJ) based on a digital-radio-frequency-memory (DRFM) device can generate false targets at the victim radar by the interception and repeating of the radar transmission signal, which is highly correlated with the true target signal. ISRJ can achieve main lobe jamming and has both deception and oppressive jamming effects, so it is difficult for the existing methods to suppress this jamming effectively. In this paper, we propose a deep-learning-based anti-jamming network, named MSMD-net (Multi-stage Multi-domain joint anti-jamming depth network), for suppressing ISRJ main lobe jamming in the radar echo. In the first stage of MSMD-net, considering that the target signal is difficult to detect under a high jamming-to-signal ratio (JSR), we propose a preprocessing method of limiting filtering on the time-frequency domain to reduce the JSR using the auxiliary knowledge of radar. In the second stage, taking advantage of the discontinuity of the jamming in the time domain, we propose a UT-net network that combines the U-net structure and the transformer module. The UT-net performs target feature extraction and signal reconstruction in the signal time-frequency domain and preliminarily realizes the suppression of the jamming component. In the third stage, combined with phase information, a one-dimensional complex residual convolution U-net network (ResCU-net) is constructed in the time domain to realize jamming filtering and signal recovery further. The experimental results show that MSMD-net can obtain the best jamming suppression effect under different transmitted signals, different jamming modes, and different jamming parameters. Full article

Smart noise jamming is a new jamming style against radar, which plays an important role in modern radar electronic warfare. In order to solve the complex problem of convolutional smart noise jamming suppression with different time delay and jamming frequency modulation, a novel jamming suppression method based on the optimized atomic dictionary decomposition by estimating significant jamming parameters is proposed in this paper. First, the dual channel jamming signal information is obtained by designing twinning waveforms, then the independent estimation of time delay and jamming frequency parameters are established based on the spatial position sequences of the false targets. After that, the atomic dictionary is optimized by utilizing the obtained jamming parameters to achieve more effective decomposition. The performance of the new jamming suppression method is analyzed and verified by experimental simulations. The results show that this method can feasibly and practicably suppress the smart noise jamming with a deceptive effect, and the atomic decomposition efficiency is highly improved. Full article