Implementation of a combinatorial‐optimisation‐based threat evaluation and jamming allocation system

You, Shixun; Diao, Ming; Gao, Lipeng

doi:10.1049/iet-rsn.2019.0001

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…In the course of flight, the control of a UAV generally adopts a three degrees of freedom model. According to reference [25], there is strong coupling between UAV velocity and distance and weak coupling between acceleration and distance. In practice, the new generation of autopilots (such as Pixhawk) can autonomously fly according to the control signal, so that the UAV can realize autonomous flight control by providing position and speed.…”

Section: Kinematics Of Uavmentioning

confidence: 99%

Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning

et al. 2022

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The reconnaissance of high-value targets is prerequisite for effective operations. The recent appreciation of deep reinforcement learning (DRL) arises from its success in navigation problems, but due to the competitiveness and complexity of the military field, the applications of DRL in the military field are still unsatisfactory. In this paper, an end-to-end DRL-based intelligent reconnaissance mission planning is proposed for dual unmanned aerial vehicle (dual UAV) cooperative reconnaissance missions under high-threat and dense situations. Comprehensive consideration is given to specific mission properties and parameter requirements through the whole modelling. Firstly, the reconnaissance mission is described as a Markov decision process (MDP), and the mission planning model based on DRL is established. Secondly, the environment and UAV motion parameters are standardized to input the neural network, aiming to deduce the difficulty of algorithm convergence. According to the concrete requirements of non-reconnaissance by radars, dual-UAV cooperation and wandering reconnaissance in the mission, four reward functions with weights are designed to enhance agent understanding to the mission. To avoid sparse reward, the clip function is used to control the reward value range. Finally, considering the continuous action space of reconnaissance mission planning, the widely applicable proximal policy optimization (PPO) algorithm is used in this paper. The simulation is carried out by combining offline training and online planning. By changing the location and number of ground detection areas, from 1 to 4, the model with PPO can maintain 20% of reconnaissance proportion and a 90% mission complete rate and help the reconnaissance UAV to complete efficient path planning. It can adapt to unknown continuous high-dimensional environmental changes, is generalizable, and reflects strong intelligent planning performance.

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Section: Kinematics Of Uavmentioning

confidence: 99%

Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning

et al. 2022

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“…You et al. [17] considered the confrontation process of jammers and radars and proposed a combinatorial‐optimisation‐based threat evaluation and jamming allocation system. Jiang et al.…”

Section: Introductionmentioning

confidence: 99%

Combination search strategy‐based improved particle swarm optimisation for resource allocation of multiple jammers for jamming netted radar system

Zou

Niu

Liu

et al. 2023

IET Signal Processing

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This paper presents a new algorithm named combination search strategy (CSS)‐based improved particle swarm optimisation (IPSO) to address the resource allocation problem of multiple jammers. First, the authors propose a CSS to effectively broaden the limited search range of the two‐step solving framework. This method not only simplifies the solution framework but also considers the combined relationship between beam pointing and transmit power to determine the global solution to the original problem directly. Second, the authors propose the IPSO because the complexity of the decision variables is increased by CSS. This method can change the focus of searching the optimal solution at different stages, correct the direction of particle evolution over time and avoid the interference between the variables. Finally, this study simulates the problem of resource allocation of multiple jammers based on the CSS‐IPSO. Based on the simulation results, the combined search strategy can obtain better resource allocation results in a short time, and the IPSO algorithm can further improve the accuracy and stability of the resource allocation results.

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“…Complex electromagnetic environment jamming is one of the most important factors [1, 2] that seriously affect the performance of radar system, since it can change the echo waveforms, decrease the signal to noise/clutter rate of the receiver, misdirect the radar antenna, and so on. To quantitatively describe the effects of jamming on the radar, many evaluation approaches, such as modelling the jamming, exploring the mechanism, analysing the assessment indices, performing simulations, etc., have been studied from theories to practical applications during the past two decades [3, 4]. The scientific and quantitative impact evaluation of the jamming on the radar not only helps for accurately describing the system effectiveness of radar in the complex electromagnetic environment, but also provides important features to recognise the jamming types and estimate the jamming parameters for anti‐jamming [5, 6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation method for impact of jamming on radar based on expert knowledge and data mining

Wang

Liu

2020

IET Radar, Sonar & Navigation

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Implementation of a combinatorial‐optimisation‐based threat evaluation and jamming allocation system

Cited by 13 publications

References 22 publications

Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning

Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning

Combination search strategy‐based improved particle swarm optimisation for resource allocation of multiple jammers for jamming netted radar system

Evaluation method for impact of jamming on radar based on expert knowledge and data mining

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