Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network

Tang, Zheng; Gao, Xiaoguang

doi:10.1016/s1004-4132(08)60142-5

Cited by 9 publications

(3 citation statements)

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“…According to [2,[27][28][29], networks are considered the most powerful fault diagnosis techniques and have been widely used in various fields, such as mine seismic event discrimination [30,31], and some works considering intrusion detection in wireless communication networks, which could also be helpful in fault localization [32]. Bayesian network techniques are also widely used in mechanical equipment [33][34][35], electronic equipment [36,37], thermal power plants [38,39], petrochemical plants [40,41], nuclear power plants [42,43], and medical diagnoses [44][45][46].…”

Section: Related Workmentioning

confidence: 99%

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

Liang

Liu

2020

Sensors

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A small fault in a large communication network may cause abrupt and large alarms, making the localization of the root cause of failure a difficult task. Traditionally, fault localization is carried out by an operator who uses alarms in alarm lists; however, fault localization process complexity needs to be addressed using more autonomous and intelligent approaches. Here, we present an overall framework that uses a message propagation mechanism of belief networks to address fault localization problems in communication networks. The proposed framework allows for knowledge storage, inference, and message transmission, and can identify a fault’s root cause in an event-driven manner to improve the automation of the fault localization process. Avoiding the computational complexity of traditional Bayesian networks, we perform fault inference in polytrees with a noisy OR-gate model (PTNORgate), which can reduce computational complexity. We also offer a solution to store parameters in a network parameter table, similar to a routing table in communication networks, with the aim of facilitating the development of the algorithm. Case studies and a performance evaluation show that the solution is suitable for fault localization in communication networks in terms of speed and reliability.

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Section: Related Workmentioning

confidence: 99%

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

Liang

Liu

2020

Sensors

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“…Inspired by the cognitive radio, the application of intelligent algorithms in radar confrontation became possible [ 1 , 5 , 6 , 7 ]. To solve the problem of the low rate of template matching under the incomplete jamming rule library condition, a jamming decision-making method based on clustering and resampling–support vector machine is proposed in [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…To solve the problem of the low rate of template matching under the incomplete jamming rule library condition, a jamming decision-making method based on clustering and resampling–support vector machine is proposed in [ 1 ]. In [ 5 ], a discrete dynamic Bayesian network is established to guide decision-making in self-defense electronic jamming. In [ 6 ], an improved chaos genetic algorithm is applied to the allocation of interference strategy.…”

Section: Introductionmentioning

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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Limiting Accuracy of Solving the Two-Dimensional Parabolic Equation by the Split-Step Fourier Transform Method

Mogilnikov

Akulinichev

2021

Russ Phys J

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Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network

Cited by 9 publications

References 0 publications

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

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

Limiting Accuracy of Solving the Two-Dimensional Parabolic Equation by the Split-Step Fourier Transform Method

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