AOA Measurement Processing for Positioning using Unmanned Aerial Vehicles

Fokin, Grigoriy

doi:10.1109/blackseacom.2019.8812834

Cited by 23 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current passive positioning system is mainly divided into angle of arrival (AOA) [7,8], received signal strength (RSS) [9,10], time difference of arrival (TDOA) [10,11], frequency difference of arrival (FDOA) [12,13] and the combination of the above positioning systems. In the actual battlefield environment, different positioning systems have different error statistics characteristics and corresponding performance.…”

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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…Dual-station cross positioning based relative localization [3] is another method which can be used in UAV swarm as long as each UAV is equipped with phased array antenna or array antenna to survey the AOA of signal from other surrounding UAVs [4]. Considering the fact that AOA value is not accurate enough to calculate positioning results utilizing traditional electrical domain measurement method, cooperative localization processing seldom applies AOA measurement.…”

Section: Introductionmentioning

confidence: 99%

Hardware-in-the-loop simulation and precision evaluation of UAV swarm cooperative localization based on AOA measurement utilizing optical phase scanning

Chen,

Li,

et al. 2023

AOPC 2023: AI in Optics and Photonics

View full text Add to dashboard Cite

Design and implementation of a novel real‐time unmanned aerial vehicle localization scheme based on received signal strength

LiuTong

Zhaohui

YangHaojun

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

To accurately localize unmanned aerial vehicles (UAVs) is one of the key issues to deal with the security threats caused by UAVs. Thus, this article proposes a UAV localization scheme that utilizes the area grid quantization and transmit power statistical calibration techniques, in which the location and transmit power of the UAV are unknown. Firstly, the adaptive discrete particle swarm optimization (ADPSO) algorithm is used to find the global optimal solution to the localization problem. A grid quantization method is proposed to discretize the value space of the traditional PSO algorithm to reduce the estimated localization time. An adaptive factor is also introduced to adjust the value of power for each iteration in the discrete particle swarm optimization (DPSO) algorithm in order to find the global optimal solution efficiently. Secondly, the fixed transmit power range is adopted to calibrate the estimated value of transmit power and the corresponding location. The prototype system comprises four anchor nodes based on a universal software radio peripheral radio frequency (USRP RF). Finally, field experiments are carried out to demonstrate the effectiveness of the proposed localization scheme.

show abstract

AOA Measurement Processing for Positioning using Unmanned Aerial Vehicles

Cited by 23 publications

References 9 publications

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

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

Hardware-in-the-loop simulation and precision evaluation of UAV swarm cooperative localization based on AOA measurement utilizing optical phase scanning

Design and implementation of a novel real‐time unmanned aerial vehicle localization scheme based on received signal strength

Contact Info

Product

Resources

About