A Practical Deceptive Jamming Method Based on Vulnerable Location Awareness Adversarial Attack for Radar HRRP Target Recognition

Du, Chuan; Yang, Cong; Zhang, Lei; Guo, Dandan; Wei, Song

doi:10.1109/tifs.2022.3170275

Cited by 11 publications

(5 citation statements)

References 41 publications

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“…This interference signal is easy to generate. It can achieve high prediction of incorrect target categories [17]. Wang Q et al proposed a dense capsule network for SAR automatic target recognition.…”

Section: Related Workmentioning

confidence: 99%

Target Recognition of Multi Source Machine Vision Pan Tilt Integrated Inspection Robot for Power Inspection

Zhu,

Zhang,

Zhou

et al. 2024

IEEE Access

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With the continuous changes in socio-economic needs, traditional methods of power facility inspection can no longer meet practical needs due to their low efficiency and lack of scalability. In response to this challenge, this study delves into the integrated motion control technology of inspection robots equipped with gimbal mechanisms, aiming to improve the convenience and efficiency of dynamic data collection. A customized multi-source heterogeneous visual detection and recognition model based on the YOLOv3 framework has been proposed, and simultaneously using path aggregation networks to enhance information processing capacity by fusing multi-scale features. Experimental analysis shows that as the robot's movement speed increases, the error rate correspondingly increases, indicating the direction of optimization. In the target recognition experiment, the proposed model achieved an average accuracy of 94.26% in visible light images and 68.05% in infrared images. In addition, Sub_ The YOLO algorithm demonstrates a fast detection speed of 30 frames per second, with an average accuracy of over 80%, marking an important progress in real-time object detection applications. In the linear motion test, the relative error of the robot's motion accuracy was 0.33% at a speed of 500 millimeters per second. However, when the speed was increased to 1200 millimeters per second, the error increased to 2.45%, indicating a significant increase in slip. This indicates that the linear motion accuracy of the robot is acceptable at low to medium speeds, but the accuracy decreases significantly at high speeds.Overall, the research results confirm the synergistic effect of integrated motion control between inspection robots and gimbals, as well as Sub_ The superiority of YOLO in target recognition has improved the ability to use wheeled robots for electrical inspections, bringing substantial technological progress to the field of autonomous inspection.

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Section: Related Workmentioning

confidence: 99%

Target Recognition of Multi Source Machine Vision Pan Tilt Integrated Inspection Robot for Power Inspection

Zhu,

Zhang,

Zhou

et al. 2024

IEEE Access

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“…The current methods of radar jamming mainly include suppression and deception jamming [1][2][3] . False target jamming [4][5][6][7] is an efficient deception jamming method.…”

Section: Introductionmentioning

confidence: 99%

A study on finite false target jamming for radar track generation

Wang,

Shi

et al. 2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

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“…However, the generation of effective false targets necessitates precise knowledge of the radar signal and target scene parameters. Uncertainties regarding the target’s position, signal-to-noise ratio (SNR), and other factors can cause misalignment between the false echoes and the true target, resulting in the degradation of the jamming impact [ 1 , 2 , 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Advancing Stepped-Waveform Radar Jamming Techniques for Robust False-Target Generation against LFM-CFAR Systems

Wang,

Shi

et al. 2023

Sensors

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This study investigates the utilization of a stepped wave frequency modulation jamming technique in radar systems. The objective is to enhance the effectiveness and robustness of false target jamming in the presence of linear frequency modulation (LFM) radars employing constant false alarm rate (CFAR) detection. The proposed method combines stepped frequency modulation with full pulse delay/sum repeat jamming to enhance resilience against uncertainties in target parameters. Theoretical analysis and simulation experiments are conducted to establish relationships between key jammer parameters, such as frequency slope and power compensation, and performance metrics, like false target distribution and CFAR masking. The results demonstrate that the proposed technique effectively maintains a dense distribution of false targets surrounding the protected target, even in the presence of uncertainties in position and signal-to-noise ratio. In comparison to existing methods, the utilization of stepped-waveform modulation enables improved control over target distribution and CFAR masking. Adaptive power allocation compensates for parameter errors, thereby enhancing robustness. Simulation results reveal that the proposed approach significantly reduces the probability of detecting the true target by over 95% under uncertain conditions, while previous methods experienced degradation. The integration of stepped waveforms optimizes false target jamming, thereby advancing electronic warfare capabilities in countering advanced radar threats. This study establishes design principles for resilient jamming architectures and supports enhanced survivability against radars employing pulse compression and CFAR detection. Moreover, the concepts proposed in this study have the potential for extension to emerging radar waveforms.

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A Practical Deceptive Jamming Method Based on Vulnerable Location Awareness Adversarial Attack for Radar HRRP Target Recognition

Cited by 11 publications

References 41 publications

Target Recognition of Multi Source Machine Vision Pan Tilt Integrated Inspection Robot for Power Inspection

Target Recognition of Multi Source Machine Vision Pan Tilt Integrated Inspection Robot for Power Inspection

A study on finite false target jamming for radar track generation

Advancing Stepped-Waveform Radar Jamming Techniques for Robust False-Target Generation against LFM-CFAR Systems

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