Deceptive jamming against multi‐channel SAR‐GMTI

Zhang, Jingke; Qi, Zhi‐mei; Zeng, Yonghu; Wang, Liandong

doi:10.1049/joe.2019.0504

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Parameter insensitivity often indicates that a method exhibits strong robustness to changes in parameters, including jamming parameters and hyperparameters. This means that even without precise parameters tuning, the jamming method can still perform well [21]- [24]. This is highly relevant in practical engineering applications where parameter choices may be constrained or fine-tuning may be difficult.…”

Section: Introductionmentioning

confidence: 99%

An Improved Reinforcement Learning Method Based on Unsupervised Learning

Chang,

Li,

Zhang

et al. 2024

IEEE Access

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The approach of directly combining clustering method and reinforcement learning (RL) will lead to encounter the issue where states may have different state transition processes under the same action, resulting in poor policy performance. To address this challenge with multi-dimensional continuous observation data, an improved reinforcement learning method based on unsupervised learning is proposed with a novel framework. Instead of dimensionality reduction methods, unsupervised clustering is employed to indirectly capture the underlying structure of the data. First, the proposed framework incorporates multidimensional information, including the current observation data, the next observation data and reward information, during the clustering process, leading to a more accurate and comprehensive low-dimensional discrete representation of the observation data while retaining preserving transition of Markov decision process. Second, by compressing the observation data into a well-defined state space, the resulting cluster labels serve as the low-dimensional discrete label-states for reinforcement learning to generate more effective and robust policies. Comparative analysis with state-of-the-art RL methods demonstrates that the improved RL methods base on framework achieves higher rewards, indicating its superior performance. Furthermore, the framework exhibits computational efficiency, as evidenced by its reasonable time complexity. This structural innovation allows for better exploration and exploitation of the transition, leading to improved policy performance in engineering applications.

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Section: Introductionmentioning

confidence: 99%

An Improved Reinforcement Learning Method Based on Unsupervised Learning

Chang,

Li,

Zhang

et al. 2024

IEEE Access

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“…To solve the aforementioned problem, some researchers employed two synergetic transponders to interfere dual-channel InSAR and SAR-GMTI system [25][26][27][28][29]. In [26,27], two synergetic transponders were used to generate a false three-dimension (3D) scene in InSAR image by modulating each transponder with complex modulation coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…In [26,27], two synergetic transponders were used to generate a false three-dimension (3D) scene in InSAR image by modulating each transponder with complex modulation coefficients. Besides, in [28,29], a new method utilizing two synergetic transponders against dual-channel SAR-GMTI was proposed, which could generate false moving targets with high fidelity. These methods are effective in jamming dual-channel SAR, but unable to work in jamming multichannel SAR.…”

Section: Introductionmentioning

confidence: 99%

Deceptive Targets Generation Simulation Against Multichannel SAR

Xing

Dai

et al. 2020

Electronics

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Traditional synthetic aperture radar (SAR) deceptive jamming can effectively generate deceptive scenes or false targets in SAR images. However, these false targets or scenes can be easily distinguished or eliminated by the multichannel SAR system. To interfere with the multichannel SAR, we first analyzed the results of SAR deceptive jamming generated by one transponder and two transponders against three-channel SAR- ground moving target indication (GMTI). Then, we propose a new deceptive jamming method against three-channel SAR-GMTI by using three synergetic transponders. By modulating each transponder with a complex coefficient, three synergetic transponders can generate false moving targets with the controllable radial velocity and located azimuth position in three-channel SAR-GMTI. Besides, in this paper, we also introduce an algorithm to deploy three transponders reasonably by utilizing the minimum condition number. In the end, a general architecture of multiple transponders deceiving multichannel SAR is given. The proposed method can not only generate deceptive false targets against multichannel SAR-GMTI, but also guide the production of a deceptive digital elevation model (DEM) against multichannel interferometric SAR (InSAR). Simulations verify the effectiveness of the proposed method.

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Antirange-Deception Jamming From Multijammer for Multistatic SAR

Wang

Pei

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Deceptive jamming against multi‐channel SAR‐GMTI

Cited by 4 publications

References 7 publications

An Improved Reinforcement Learning Method Based on Unsupervised Learning

An Improved Reinforcement Learning Method Based on Unsupervised Learning

Deceptive Targets Generation Simulation Against Multichannel SAR

Antirange-Deception Jamming From Multijammer for Multistatic SAR

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