Algorithm for Positioning in Non-line-of-Sight Conditions Using Unmanned Aerial Vehicles

Fokin, Grigoriy; Ali, Al-odhari Abdulwahab Hussain

doi:10.1007/978-3-030-01168-0_44

Cited by 16 publications

(2 citation statements)

References 15 publications

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“…The mode of positioning and then implementing the strike is realistic and feasible [4]. Compared with the use of space-borne platforms for positioning, the advantages of using UAV swarm for passive positioning are mainly [5,6]:…”

Section: Introductionmentioning

confidence: 99%

Hybrid TDOA/FDOA and track optimization of UAV swarm based on A-optimality

Li¹,

Sun²,

Zhou³

et al. 2023

J. of Syst. Eng. Electron.

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The source location based on the hybrid time difference of arrival (TDOA)/frequency difference of arrival (FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle (UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision (GDOP) factor. Second, the Cramer-Rao lower bound (CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.

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Section: Introductionmentioning

confidence: 99%

Hybrid TDOA/FDOA and track optimization of UAV swarm based on A-optimality

Li¹,

Sun²,

Zhou³

et al. 2023

J. of Syst. Eng. Electron.

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“…As traditional radiation source localization technologies mostly rely on fixed base stations on the ground to receive signals, the geometric positions of the base stations render it impossible to complete the localization task in remote areas, with only a few base stations. Compared with large active localization systems, small and light passive localization systems are more suitable for UAV platforms [2,3]. UAV passive localization systems can improve positioning flexibility and solve the problem of poor positioning accuracy at long distances from the fixed base stations.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Selection Synchronization Mechanism-Based Moving Source Localization Using UAV Swarm

et al. 2023

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To obtain the accurate time difference of arrival (TDOA) and frequency difference of arrival (FDOA) for passive localization in an unmanned aerial vehicle (UAV) swarm, UAV swarm network synchronization is necessary. However, most of the traditional distributed time synchronization protocols are based on iteration, which hinders efficiency improvement. High communication costs and long convergence times are often required in large-scale UAV swarm networks. This paper presents a neighborhood selection-all selection (NS-AS) synchronization mechanism-based moving source localization method for UAV swarms. First, the NS-AS synchronization mechanism is introduced, to model the UAV swarm network synchronization process. Specifically, the UAV neighbors are first grouped by sector, and the most representative neighbors are selected from each sector for the state update calculation. When the UAV swarm network reaches a fully connected state, the synchronization mechanism is switched to select all neighbors, to improve the convergence speed. Then, the TDOA-FDOA joint localization algorithm is employed to locate the moving radiation source. Through simulation, the effectiveness of the proposed method is verified by the system convergence and localization performance under different parameters. Experimental results show that the synchronization mechanism based on NS-AS effectively improves the convergence speed of the system while ensuring the accuracy of moving radiation source localization.

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