Researching UAV Threat – New Challenges

Farlik, Jan; Gacho, Lukáš

doi:10.1109/icmt52455.2021.9502759

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…According to the specific PDF in (16) and substituting from (11), (19) can be derived as: (24) where…”

Section: Neyman-pearson Detection Modelmentioning

confidence: 99%

“…It plays a great promotion role in the rapid deployment of selfpropelled radar equipment and air defense planning in key areas, which are critical military needs with significant applications. In particular, it is utilized in air defense operations to enhance combat efficiency within the restrictions of complex battle characteristics, such as electromagnetics, topography, and hostile situations [11,12].…”

Section: Introduction 1background and Related Studiesmentioning

confidence: 99%

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Joint Antenna Placement and Power Allocation for Target Detection in a Distributed MIMO Radar Network

Xie

Zhang

2022

Remote Sensing

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Radar network configuration and power allocation are of great importance in military applications, where the entire surveillance area needs to be searched under resource budget constraints. To pursue the joint antenna placement and power allocation (JAPPA) optimization, this paper develops a JAPPA strategy to improve target detection performance in a widely distributed multiple-input and multiple-output (MIMO) radar network. First, the three variables of the problem are incorporated into the Neyman–Pearson (NP) detector by using the antenna placement optimization and the Lagrange power allocation method. Further, an improved iterative greedy dropping heuristic method based on a two-stage local search is proposed to solve the NP-hard issues of high-dimensional non-linear integer programming. Then, the sum of the weighted logarithmic likelihood ratio test (LRT) function is constructed as optimization criteria for the JAPPA approach. Numerical simulations and the theoretical analysis confirm the superiority of the proposed algorithm in terms of achieving effective overall detection performance.

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“…According to the specific PDF in (16) and substituting from (11), (19) can be derived as: (24) where…”

Section: Neyman-pearson Detection Modelmentioning

confidence: 99%

Section: Introduction 1background and Related Studiesmentioning

confidence: 99%

Joint Antenna Placement and Power Allocation for Target Detection in a Distributed MIMO Radar Network

Xie

Zhang

2022

Remote Sensing

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“…This new type of counter-UAV covers advanced technologies including intelligent decision making and control and has certain advantages. For large-scale UAV attacks, interception based on fixed-wing UAV platforms will become one of the most important means of countering [3,4]. Task assignment, as the essential component of the multi-UAV system, plays a vital role in the optimal configuration of targets and UAVs in interception scenarios [5].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Task Assignment for Multi-UAV System in Large-Scale Group-to-Group Interception Scenarios

Wu,

Zhang,

Wang

et al. 2023

Drones

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The multi-UAV task assignment problem in large-scale group-to-group interception scenarios presents challenges in terms of large computational complexity and the lack of accurate evaluation models. This paper proposes an effective evaluation model and hierarchical task assignment framework to address these challenges. The evaluation model incorporates the dynamics constraints specific to fixed-wing UAVs and improves the Apollonius circle model to accurately describe the cooperative interception effectiveness of multiple UAVs. By evaluating the interception effectiveness during the interception process, the assignment scheme of the multiple UAVs could be given based on the model. To optimize the configuration of UAVs and targets, a hierarchical framework based on the network flow algorithm is employed. This framework utilizes a clustering method based on feature similarity and interception advantage to decompose the large-scale task assignment problem into smaller, complete submodels. Following the assignment, Dubins curves are planned to the optimal interception points, ensuring the effectiveness of the interception task. Simulation results demonstrate the feasibility and effectiveness of the proposed scheme. With the increase in the model scale, the proposed scheme has a greater descending rate of runtime. In a large-scale scenario involving 200 UAVs and 100 targets, the runtime is reduced by 84.86%.

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