UAV Path Planning for AOA-based Source Localization with Distance-Dependent Noises

Yan ZUO; Xuejiao LIU; Dongliang PENG

doi:10.11999/JEIT200078

Volume 43 Issue 4

Apr. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(4): 1192-1198

Yan ZUO, Xuejiao LIU, Dongliang PENG. UAV Path Planning for AOA-based Source Localization with Distance-Dependent Noises[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1192-1198. doi: 10.11999/JEIT200078

Citation:

Yan ZUO, Xuejiao LIU, Dongliang PENG. UAV Path Planning for AOA-based Source Localization with Distance-Dependent Noises[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1192-1198. doi: 10.11999/JEIT200078

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UAV Path Planning for AOA-based Source Localization with Distance-Dependent Noises

doi: 10.11999/JEIT200078

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China (61673146, 61771028, 61973102), Fund of Science and Technology on Electronic Information Control Laboratory (6142105200102)

Received Date: 2020-01-19
Rev Recd Date: 2020-06-01

Available Online: 2020-12-11

Publish Date: 2021-04-20

Abstract

Abstract

An optimal path planning problem is investigated for Angle-Of-Arrival (AOA) source localization using Unmanned Aerial Vehicles (UAVs) equipped with passive sensors. The more realistic model is considered where the variance of AOA measurement noises is a function of the source-to-sensor distances, which complicates AOA-based source localization. A modified Variable Gain Unscented Kalman Filter (VG-UKF)is developed to adapt to distance-dependent variance of AOA measurement noises. The Generalized Cramer-Rao Lower Bound (GCRLB) of AOA localization is calculated. Further, the unconstrained optimal sensor placement and constrained optimal sensor placement are theoretically analyzed. Then a path planning model for UAVs is constructed with minimizing the trace of GCRLB, which is solved optimally with penalty function and LM (Levenberg-Marquardt) algorithm. The effectiveness of the proposed algorithm is illustrated with simulation results.
- Source localization,
- Angle of Arrival (AOA),
- Distance-dependent noises,
- Generalized Cramer-Rao Lower Bound (GCRLB),
- Path Planning

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References(19)

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