Remote Interference Source Localization: A Multi‐UAV‐Based Cooperative Framework

Wu, Guangyu; Gu, Jiangchun

doi:10.1049/cje.2021.00.310

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…And Zhang et al [27] proposed an energysaving deployment algorithm by balancing the flight distance and final service altitude of heterogeneous MUAVs to maximize residual energy. Huang et al [28] studied the UAV pair-supported relaying problem in IoT systems, which receiver is used as relay between transmitter and destination, and proposed dueling DDQN method to solve the optimization problem. Wu et al [29] proposed a multi-UAV-based cooperative framework to balance the accuracy and efficiency of UAVs searching and localization, and studied two algorithms to decide UAVs flight direction and estimate the position of interference source.…”

Section: Uavs Deployment and Energy Optimizationmentioning

confidence: 99%

Multi-Objective Coordinated Optimization for UAV Charging Scheduling in Intelligent Aerial-Ground Perception Networks

Yi,

Xiang,

Huaguang

et al. 2023

Chinese J. Elect.

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The unmanned aerial vehicles (UAVs)assisted intelligent traffic perception system can provide effective situation awareness. However, UAVs are required to be recharged before the energy is exhausted, which may cause task interruption. To address this concern, the charging UAV (CUAV) is employed to provide wireless charging for the mission UAVs (MUAVs). This paper studies the charging scheduling problem of the CUAV under the premise of optimizing the MUAVs deployment. We first model the MUAVs deployment problem considering the energy consumption and data transmission and establish the CUAV charging model. Then, the above problem is formulated as a multi-objective multi-agent stochastic game process to simplify the decisions-making of MUAVs and CUAV, based on which we propose the utility-based Pareto optimal deployment and charging algorithm, which reduces the computing complexity by equivalent utility of the MUAVs while using Kullback-Leibler divergence to constrain solutions. Next, to ensure the effectiveness of policy update, the multi-agent communication protocol is adopted to improve policy exploration efficiency. Simulation results show that the proposed algorithm outperforms existing works in terms of energy efficiency and charging by comparing with the Pareto front of different methods, endurance anxiety of the MUAVs, and charging utilization under different task modes.

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Section: Uavs Deployment and Energy Optimizationmentioning

confidence: 99%

Multi-Objective Coordinated Optimization for UAV Charging Scheduling in Intelligent Aerial-Ground Perception Networks

Yi,

Xiang,

Huaguang

et al. 2023

Chinese J. Elect.

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“…Low complexity Q-learning and FFT. [13] Alleviation of limitations in SDR deployment. Markov random field framework.…”

Section: Research Topic On Uav-based Sensing Modeling Approach Refere...mentioning

confidence: 99%

“…This scheme is carried out while considering collision and energy causality constraints. In the context of swarm UAVs, [ 13 ] suggests a pioneering collaborative search and localization approach. This method relies on a low-complexity Q-learning algorithm and a fast Fourier transformation (FFT)-based location prediction algorithm.…”

Section: Introductionmentioning

confidence: 99%

UAV aided virtual cooperative spectrum sensing for cognitive radio networks

Gul,

Kim,

Ali

et al. 2023

PLoS ONE

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Cooperative spectrum sensing (CSS) involves multiple secondary users (SUs) reporting primary user (PU) channel sensing states to the fusion center (FC). However, the high overheads associated with multi-user CSS impose power limitations that limit its usefulness in unmanned aerial vehicle (UAV) networks. To address this challenge, we propose a virtual CSS, where a single UAV conducts CSS while following a circular flight trajectory in the air. The novelty of our approach is presenting a working frame structure for the UAV flight, including sensing and data transmission periods with further division of the sensing time into mini-sensing slots. In the virtual CSS, UAV performs local sensing decisions in each mini-slot and accumulates them for a final decision. The proposed virtual CSS scheme exploits sequential decision fusion (SDF), which sequentially adds individual mini-slot decisions. Additionally, we leverage machine learning (ML), employing AdaBoost ensembling classifier (ENC), to inspect flight conditions and reconfigure mini-slot periods dynamically for both traditional decision fusion (TDF) and our proposed SDF schemes. Furthermore, we identify an optimal decision threshold (ODT) for the proposed SDF, enabling the comparison of sequential results with an adjustable threshold through majority voting. This novel approach results in energy efficiency and improved throughput for virtual CSS using SDF, surpassing the performance of TDF, which relies on collecting entire mini-slot reports for its final decision. Simulation results demonstrate the effectiveness of the proposed SDF following the ENCODT (SDF-ENCODT) scheme compared to existing techniques from the literature. We explore varying levels of UAV flight velocities, moving radius, detection probability demand, and channel signal-to-noise ratio (SNR), reinforcing the significance of our contribution. Our research highlights the motivation to address spectrum scarcity in UAV communication by proposing an innovative virtual CSS scheme based on SDF. The proposed approach enhances spectrum utilization, overcomes power limitations, and substantially improves CSS for UAV networks.

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“…Some researchers have designed ground interference monitoring platforms consisting of ground monitoring stations and monitoring vehicles, which can effectively measure the direction of interference signals and locate interference signals [13]. For airborne interference monitoring platforms, unmanned aerial vehicles are used to locate the interference source, which can eliminate the influence of terrain and effectively approach the interference source to a certain extent [14][15][16]. In the field of satellite navigation, research on interference source localization algorithms includes the utilization of adaptive filtering recursive least squares combined with generalized cross-correlation methods for delay estimation, as well as the use of TDOA algorithms for range localization of interference sources [17].…”

Section: Introductionmentioning

confidence: 99%

Satellite Interference Source Direction of Arrival (DOA) Estimation Based on Frequency Domain Covariance Matrix Reconstruction

Yao,

Zhao,

Bai

et al. 2023

Sensors

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Direction of arrival (DOA) estimation is an effective method for detecting various active interference signals during the satellite navigation process. It can be utilized for both interference detection and anti-interference applications. This paper proposes a DOA estimation algorithm for satellite interference sources based on frequency domain covariance matrix reconstruction (FDCMR) to address various types of active interference that may occur in the satellite navigation positioning process. This algorithm can estimate the DOA of coherent signals from multiple frequency points under low signal-to-noise ratio (SNR) conditions. The signals received from the array are transformed from the time domain to the frequency domain using a fast Fourier transform (FFT). The data corresponding to the frequency point of the target signal is extracted from the signal in the frequency domain. The frequency domain covariance matrix of the received array signals is reconstructed by utilizing its covariance matrix property. The spatial spectrum search method is used for the final DOA estimation. Simulation experiments have shown that the proposed algorithm performs well in the DOA estimation under low SNR conditions and also resolves coherency. Moreover, the algorithm’s effectiveness is verified through comparison with three other algorithms. Finally, the algorithm’s applicability is validated through simulations of various interference scenarios.

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Remote Interference Source Localization: A Multi‐UAV‐Based Cooperative Framework

Cited by 8 publications

References 29 publications

Multi-Objective Coordinated Optimization for UAV Charging Scheduling in Intelligent Aerial-Ground Perception Networks

Multi-Objective Coordinated Optimization for UAV Charging Scheduling in Intelligent Aerial-Ground Perception Networks

UAV aided virtual cooperative spectrum sensing for cognitive radio networks

Satellite Interference Source Direction of Arrival (DOA) Estimation Based on Frequency Domain Covariance Matrix Reconstruction

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