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

doi:10.23919/JSEE.2023.000008

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (1): 149-159.doi: 10.23919/JSEE.2023.000008

• SYSTEMS ENGINEERING • Previous Articles Next Articles

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

Hao LI(), Hemin SUN(), Ronghua ZHOU(), Huainian ZHANG()

¹ Department of Intelligence, Air Force Early Warning Academy, Wuhan 430019, China

Received:2020-12-29 Online:2023-02-18 Published:2023-03-03
Contact: Ronghua ZHOU E-mail:afeu-li@163.com;skylar1013@126.com;915216235@qq.com;1241761040@qq.com
About author:
LI Hao was born in 1981. He received his B.E. degree of radar engineering, and M.E. degree of information and communication engineering from Air Force Early Warning Academy, Wuhan, China. He received his Ph.D. degree in electronic science and technology from Air Force Engineering University in 2017, Xi’an, China. His research interests are swarm intelligence, UAV swarm, intelligence systems, and signal processing. E-mail: afeu-li@163.com

SUN Hemin was born in 1972. He received his B.E. degree of radar engineering, and M.E. of degree weapon systems and applications from Air Force Early Warning Academy, Wuhan, China. His research interests are radar engineering and intelligence processing. E-mail: skylar1013@126.com

ZHOU Ronghua was born in 1996. He received his B.E. degree of radar engineering from Air Force Early Warning Academy, Wuhan, China. He is pursuing his master degree in Air Force Early Warning Academy. His research interests are swarm intelligence, blind source separation, and intelligent optimization. E-mail: 915216235@qq.com

ZHANG Huainian was born in 1984. He received his B.E. and M.E. degrees from the Second Artillery Command College, Wuhan, China. He is currently studying for his Ph.D. degree in information science at the Air Force Early Warning Academy, with a research direction of information fusion. His research interests are radar engineering and intelligence processing. E-mail: 1241761040@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61502522), Equipment Pre-Research Field Fund (JZX7Y20190253036101), Equipment Pre-Research Ministry of Education Joint Fund (6141A02033703), and Hubei Provincial Natural Science Foundation (2019CFC897)

Abstract

Abstract:

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.

Key words: unmanned aerial vehicle (UAV) swarm, time difference of arrival (TDOA), frequency difference of arrival (FDOA), A-optimality, track optimization

Hao LI, Hemin SUN, Ronghua ZHOU, Huainian ZHANG. Hybrid TDOA/FDOA and track optimization of UAV swarm based on A-optimality[J]. Journal of Systems Engineering and Electronics, 2023, 34(1): 149-159.

Figures/Tables 10

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

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