Ground threat evaluation and jamming allocation model with Markov chain for aircraft

Han, Lu; Ning, Qian; Chen, Bingcai; Lei, Yinjie; Zhou, Xin

doi:10.1049/iet-rsn.2019.0433

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…To verify the proposed algorithms, a radar parameter template is firstly created, shown in Table 1 . Referring to [ 25 , 26 ], four kinds of radar work modes are considered in this paper, including search, acquisition, tracking, and guidance. For the target, the work mode of guidance has the highest threat level, followed by tracking, acquisition, and search.…”

Section: Numerical Resultsmentioning

confidence: 99%

Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar

Liu

Zhang

et al. 2021

Sensors

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Modern adaptive radars can switch work modes to perform various missions and simultaneously use pulse parameter agility in each mode to improve survivability, which leads to a multiplicative increase in the decision-making complexity and declining performance of the existing jamming methods. In this paper, a two-level jamming decision-making framework is developed, based on which a dual Q-learning (DQL) model is proposed to optimize the jamming strategy and a dynamic method for jamming effectiveness evaluation is designed to update the model. Specifically, the jamming procedure is modeled as a finite Markov decision process. On this basis, the high-dimensional jamming action space is disassembled into two low-dimensional subspaces containing jamming mode and pulse parameters respectively, then two specialized Q-learning models with interaction are built to obtain the optimal solution. Moreover, the jamming effectiveness is evaluated through indicator vector distance measuring to acquire the feedback for the DQL model, where indicators are dynamically weighted to adapt to the environment. The experiments demonstrate the advantage of the proposed method in learning radar joint strategy of mode switching and parameter agility, shown as improving the average jamming-to-signal radio (JSR) by 4.05% while reducing the convergence time by 34.94% compared with the normal Q-learning method.

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Section: Numerical Resultsmentioning

confidence: 99%

Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar

Liu

Zhang

et al. 2021

Sensors

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“…The jamming process can be represented as a quaternion {S, A, P, R} through a finite Markov decision process (MDP) [17,18]. Here, S is a finite collection of radar states, with state s ∈ S determined by the radar pulse parameters (Fc, Pw, PRF, BW), and A is a finite set of jammer actions, where action a ∈ A is defined by the jamming pulse parameters.…”

Section: Q-learning Methods For Jamming Policymentioning

confidence: 99%

An Electronic Jamming Method Based on a Distributed Information Sharing Mechanism

2023

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In an electronic jamming system, the ability to adequately perceive information determines the effectiveness of an electronic countermeasures strategy. This paper proposes a new method based on the combination of a multi-agent electronic jammer and an information sharing mechanism. With the development of intelligent technology and deep learning, these technologies have been applied in electronic countermeasure game systems. Introducing intelligent technology into the electronic confrontation system can greatly improve decision-making efficiency. At the same time, a multi-agent electronic countermeasure cooperative system based on the information sharing method can break through the limited information perception capabilities of a single agent, thereby greatly improving the survivability of jamming systems in electronic warfare. Experimental results show that our method requires a lower jamming-to-signal ratio than the single jammer method to achieve effective electronic jamming. In addition, the electronic jamming parameters can be updated automatically as the external electromagnetic environment changes quickly, realizing a more intelligent electronic jamming system.

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“…In Ref. [42], Lu Han et al considered that the jamming strategy and environment interaction in threat evaluation and jamming allocation (TEJA) are a Markov chain model, and they proposed a new jamming strategy based on this model. This work believes that the anti-jamming strategy and the interaction of the jamming environment also have Markov properties, which can be analysed as a Markov Decision Processes (MDP) [43].…”

Section: Cognitive Radar Anti-jamming Modelmentioning

confidence: 99%

“…In Ref. [42], Lu Han et al. considered that the jamming strategy and environment interaction in threat evaluation and jamming allocation (TEJA) are a Markov chain model, and they proposed a new jamming strategy based on this model.…”

Section: Modelmentioning

confidence: 99%

Design of anti‐jamming decision‐making for cognitive radar

Wang,

Chen,

2023

IET Radar Sonar & Navi

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With the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed.

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Ground threat evaluation and jamming allocation model with Markov chain for aircraft

Cited by 9 publications

References 14 publications

Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar

Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar

An Electronic Jamming Method Based on a Distributed Information Sharing Mechanism

Design of anti‐jamming decision‐making for cognitive radar

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