Search Results (67)

Under jamming environments, the simulation system of monopulse radar consumes substantial computational resources due to echo signal and jamming signal generation, as well as real-time radar signal processing, leading to large time consumption in evaluating the radar’s anti-jamming performance in complex electromagnetic jamming scenarios. This paper proposes a monopulse radar simulation strategy based on space slicing to improve simulation efficiency. By considering the operational characteristics of the monopulse radar, including search, acquisition, track, and narrow search modes, the space where radar and target are located is divided into discrete grid points in different granularity. The simulation results for each slice are used to replace full-process real-time signal processing, thus improving the overall efficiency of the simulation system. The estimation errors of the target’s range, velocity, and angular after space slicing are theoretically analyzed. Simulation experiments demonstrate that, by utilizing the proposed space slicing strategy, the simulation speed is improved dramatically, with target parameter estimation errors remaining relatively small compared to full-process real-time simulations. Full article

Due to inaccurate reconnaissance parameters of the jammer and the decline in equipment performance over long-term usage, the false targets generated by the jammer usually have problems such as defocusing, abnormal brightness, and distortion, which fail to reach the ideal state. Therefore, experts or specialized assessment methods are needed to evaluate the interference effect. However, the human eye is not sensitive to minor changes. In such cases, the results obtained by some evaluation methods may be inconsistent with expert judgments. Therefore, this paper proposes an evaluation method that integrates three features of image edge information, brightness variation, and texture structure. This method mainly achieves two purposes: (1) Maintain a high evaluation effect when the image error does not change significantly, and be consistent with the expert’s judgment. (2) When the image error changes significantly, it can conduct effective effect evaluation like other methods, and has stronger universality and robustness. Furthermore, its robustness is reflected in the ability to reliably evaluate even in the presence of severe image distortion, without relying on geometric correction. Simulation experiments are conducted under three error conditions, and result comparisons verify the advantages and robustness of the proposed method. Finally, the effectiveness of proposed method is verified using real jamming data. Full article

The evaluation of synthetic aperture radar (SAR) jamming effectiveness is a primary means to measure the reliability of jamming effects, and it can provide important guidance for the selection of jamming strategies and application of jamming styles. To address problems in traditional evaluation methods for SAR scene deception jamming, namely the simple adoption of native feature parameters, incomprehensive integration for evaluation indicator design, and the inconsideration of resulting jamming detection effects, this paper proposes a SAR scene deceptive jamming evaluation method oriented to jamming detection. First, four profound feature parameters including the brightness change gradient, texture direction contrast degree, edge matching degree, and noise suppression difference index are extracted in terms of visual and non-visual manners, which accurately highlight the differences between jamming and the background. Subsequently, through nonlinear iterative optimization and loss function design, a comprehensive evaluation indicator, i.e., with a convex decomposition is proposed, which can effectively quantify the contribution of each feature parameter and distinguish the differences in jamming concealment under different scenes. Finally, based on the measured and simulated MiniSAR datasets of urban, mountainous, and other complex scenes, a mapping correlation between the SDD and jamming detection rate is established. The evaluation results show that when the SDD is less than 0.4, the jamming is undetectable; when the SDD is greater than 0.4, for every 0.1 increase in the SDD, the jamming detection rate decreases by approximately 0.1. This provides support for the quantification of jamming effects in terms of detection rate in real applications. Full article

With the booming development of the low-altitude economy and the widespread application of Unmanned Aerial Vehicles (UAVs), integrated sensing and communication (ISAC) technology plays an increasingly pivotal role in intelligent communication networks. However, low-altitude platforms supporting ISAC, such as UAV swarms, are highly vulnerable to deception jamming in complex electromagnetic environments. Existing multistatic radar systems face challenges in processing slowly fluctuating targets (like low-altitude UAVs) and adapting to complex electromagnetic environments when fusing multiple pulse echoes. To address this issue, targeting the protection needs of low-altitude targets like UAVs, this paper leverages the characteristic of rapid amplitude fluctuation in frequency-agile radar echoes to analyze the differences between true and false targets in multistatic frequency-agile radar systems, particularly for slowly fluctuating UAV targets, demonstrating the feasibility of discrimination. Building on this, we introduce a neural network approach to deeply extract discriminative features from true and false target echoes and propose a neural network-based variable-length processing method for deception jamming discrimination in multistatic frequency-agile radar. The simulation results show that the proposed method effectively exploits deep-level echo features, significantly improving the discrimination probability between true and false targets, especially for slowly fluctuating UAV targets. Crucially, even when trained on a fixed number of pulses, the model can process input data with varying pulse counts, greatly enhancing its practical deployment capability in dynamic UAV mission scenarios. Full article

To address the vulnerability of single-polarization deception jamming and simply modulated dual-polarization jamming to discrimination by dual-polarization radars, this paper proposes a deception jamming method based on joint fast–slow-time polarization modulation (FSPMJ). First, in the slow-time domain (across multiple pulses), the polarization azimuth of the jamming signal is designed according to the target’s polarization ratio distribution. Subsequently, with the target polarization degree as the optimization objective, the polarization phase difference of the jamming signal is solved using an interior-point optimization algorithm, establishing the initial polarization state for each pulse. This process is iterated to design the polarization state for the first half of each pulse. Then, in the fast-time domain (within a single pulse), a polarization state orthogonal to the pre-generated first-half state, is constructed to serve as the polarization state for the latter half of each pulse. Finally, the effectiveness of the proposed method is validated through combined simulation and measured data using a Support Vector Machine (SVM) algorithm. Results demonstrate that compared to single-polarization deception jamming and existing polarization-modulated jamming, this method reduces the false target discrimination rate of dual-polarization radars by 35.4% without requiring complex target scattering matrices. Full article

Accurate estimation of Interrupted Sampling Repeater Jamming (ISRJ) sampling duration is essential for effective radar anti-jamming. However, in complex electromagnetic environments, the simultaneous presence of suppressive and deceptive jamming, coupled with significant signal overlap in the time–frequency domain, renders ISRJ separation and parameter estimation considerably challenging. To address this challenge, this paper proposes a method utilizing the Fractional Fourier Transform (FRFT) and a Hybrid Modality Fusion Network (HMFN) for ISRJ signal separation and sampling-duration estimation. The proposed method first employs FRFT and a time–frequency mask to separate the ISRJ and target echo from the mixed signal. This process effectively suppresses interference and extracts the ISRJ signal. Subsequently, an HMFN is employed for high-precision estimation of the ISRJ sampling duration, offering crucial parameter support for active electromagnetic countermeasures. Simulation results validate the performance of the proposed method. Specifically, even under strong interference conditions with a Signal-to-Jamming Ratio (SJR) of −5 dB for deceptive jamming and as low as −10 dB for suppressive jamming, the regression model’s coefficient of determination still reaches 0.91. This result clearly demonstrates the method’s robustness and effectiveness in complex electromagnetic environments. Full article

Aiming at the problem that the anti-range-deception-jamming effect of a networked moving radar system is severely affected by the spatial distribution of each radar, an anti-range-deception-jamming method for networked moving radar based on trajectory optimization is proposed. Firstly, the anti-jamming method of networked moving radar considering the radar position error (RPE) is proposed. Then, the theoretical expression for the false target (FT) misjudgment probability of networked moving radar is deduced. Based on the theoretical expression, a trajectory optimization model is formulated to minimize FT misjudgment probability. Simulation experiments validate both the correctness of the derived probability expression and the significant influence of the radar spatial distribution position on the FT misjudgment probability. Moreover, the simulation results verify that the proposed anti-jamming method can effectively reduce the FT misjudgment probability of networked moving radar under the condition of a high discrimination probability of the physical target (PT). Full article

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

The transponder-style deception jamming implemented by Digital Radio Frequency Memory (DRFM) exhibits high similarity to real target radar echoes, while traditional detection methods suffer severe performance degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a DRFM active deception jamming detection method based on diagonal integral bispectrum, aiming to overcome the bottleneck of jamming detection under low-SNR conditions. By establishing a harmonic effect signal model for DRFM deception jamming, the cross-term generation mechanism in the bispectrum domain is revealed: the jamming signal generates dense cross-terms due to harmonic distortion, whereas the real target energy exhibits single-peak aggregation. To quantify this difference, the Diagonal Integral Bispectrum Relative Peak Height (DIBRP) is proposed to characterize the energy aggregation of true and false targets in the diagonal integral bispectrum, and the Diagonal Integral Bispectrum Approximate Entropy (DIBAE) is introduced to describe their complexity. A joint detection framework combining the DIBRP-DIBAE dual-feature space and a polynomial kernel support vector machine (SVM) is constructed to achieve active deception jamming detection. The proposed method demonstrates excellent performance under low-SNR conditions. Simulations and experimental results show that the correct detection rate reaches 92% at a jamming-to-signal ratio (JSR) and SNR of 0 dB, validating the effectiveness of the algorithm. 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

Range gate pull-off (RGPO) jamming is an effective method for track deception aimed at radar systems. Nevertheless, enhancing the effectiveness of the jamming strategy continues to pose challenges, restricting the RGPO jamming method from achieving its maximum potential. This paper focuses on addressing the problem of optimizing the strategy for white-box RGPO jamming, serving as a foundational step toward quantitative optimization research on RGPO jamming strategies. In the white-box scenario, it is presumed that the jammer has full knowledge of the target radar’s tracking system, encompassing both the choice of tracking method and its parameter configurations. The intricate interactions between the jammer and the tracking radar introduce three primary challenges: (1) Formulating an algebraic expression for the objective function of the jamming strategy optimization is nontrivial; (2) Direct observation of jamming effects from the target radar is challenging; (3) Noise renders the jamming outcomes unpredictable. To tackle these challenges, this study formulates the optimization of the RGPO jamming strategy as an adversarial stochastic simulation optimization (ASSO) problem and introduces a novel solution for the white-box RGPO jamming strategy optimization: a local simulation-assisted particle swarm optimization algorithm with an equal resampling scheme (PSO-ER). The PSO-ER algorithm searches for optimal jamming strategies while utilizing a localized simulation of the tracking radar to evaluate the effectiveness of candidate jamming strategies. Experiments conducted on four benchmark cases confirm that the proposed approach is capable of generating well-tuned strategies for white-box RGPO jamming. Full article

Multi-station radar can provide better performance against deception jamming, but the harsh detection requirements and risk of network destruction undermine the practicability of the multi-station radar. Therefore, it is necessary to further explore the anti-deception jamming performance of a single-station radar. This paper introduces a novel method, based on parameter estimation with a virtual multi-station system, to discriminate range deceptive jamming. The system consists of a single-station radar assisted by the reconfigurable intelligent surfaces (RIS). A unified parameter estimation model for true and false targets is established, and the convex optimization method is applied to estimate the target location and deception range. The Cramer–Rao lower bound (CRLB) of the target localization and the measured deception range is then derived. By using the measured deception range and its CRLB, an optimal discrimination algorithm in accordance with the Neyman–Pearson lemma is designed. Simulation results demonstrate the feasibility of the proposed method and analyze the effects of factors such as signal-to-noise ratio (SNR), deception range, jammer location, and the RISs station arrangement on the discrimination performance. Full article

Synthetic aperture radar (SAR) inter-pulse parameter agility technology involves dynamically adjusting parameters such as the pulse width, chirp rate, carrier frequency, and pulse repetition interval within a certain range; this effectively increases the complexity and uncertainty of radar waveforms, thereby countering active deceptive interference signals from multiple dimensions. With the development of active deceptive interference technology, single-parameter agility can no longer meet the requirements, making multi-parameter joint agility one of the main research directions. However, inter-pulse carrier frequency agility can cause azimuth Doppler chirp rate variation, making azimuth compression difficult and compensation computationally intensive, thus hindering imaging. Additionally, pulse repetition interval (PRI) agility leads to non-uniform azimuth sampling, severely deteriorating image quality. To address these issues, this paper proposes a multi-parameter agile SAR imaging scheme based on traditional frequency domain imaging algorithms. This scheme can handle joint agility of pulse width, chirp rate polarity, carrier frequency, and PRI, with relatively low computational complexity, making it feasible for engineering implementation. By inverting SAR images, the echoes with multi-parameter joint agility are obtained, and active deceptive interference signals are added for processing. The interference-suppressed imaging results verify the effectiveness of the proposed method. Furthermore, simulation results of point targets with multiple parameters under the proposed processing algorithm show that the peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are improved by 12 dB and 10 dB, respectively, compared to the traditional fixed waveform scheme. Full article