Cooperative Fusion Localization of a Nonstationary Target for Multiple UAVs Without GPS

This article studies a global positioning system (GPS)-free distributed localization problem for a nonstationary target using a cluster of unmanned aerial vehicles (UAVs) loaded with bearing sensors, which aims to cooperatively estimate the relative positions of target by local interactions, regardless of whether or not the target can be directly detected. First, for leader UAVs that can readily detect the target, a novel bearing-based estimator devised in a local frame is proposed by following a prediction and correction configuration, while a sufficient condition is established to assure the asymptotic decaying of position estimation error. Second, considering follower UAVs that cannot directly observe the target, a special consensus-based cooperative fusion algorithm comprised of coupled observation and localization subsystems is proposed for UAVs to synchronize the target estimation with neighbors’ localization, wherein a fixed-time distributed observer is delicately constructed to provide target speed estimates, such that the requirements on the global availability of target speed can be avoided. The remarkable merit is that without resorting to GPS, all members can reach an agreement on relative positioning estimates in a distributed execution sense. Lyapunov approach certifies that all errors can exponentially approximate to the origin. Simulations confirm the efficacy of the presented algorithm.

Authors

Institutions

Publication Details

Journal
IEEE Systems Journal
Published
2024-09-09
DOI
https://doi.org/10.1109/jsyst.2024.3445377
Citations
9
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
3.13

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cooperative Fusion Localization of a Nonstationary Target for Multiple UAVs Without GPS

Fei Zhang, Wendong Zhang, Xingling Shao
9 citations
IEEE Systems Journal
Target Tracking and Data Fusion in Sensor Networks
3.13
article

Cooperative Fusion Localization of a Nonstationary Target for Multiple UAVs Without GPS

Fei Zhang, Wendong Zhang, Xingling Shao
article en
9 citations

Abstract

This article studies a global positioning system (GPS)-free distributed localization problem for a nonstationary target using a cluster of unmanned aerial vehicles (UAVs) loaded with bearing sensors, which aims to cooperatively estimate the relative positions of target by local interactions, regardless of whether or not the target can be directly detected. First, for leader UAVs that can readily detect the target, a novel bearing-based estimator devised in a local frame is proposed by following a prediction and correction configuration, while a sufficient condition is established to assure the asymptotic decaying of position estimation error. Second, considering follower UAVs that cannot directly observe the target, a special consensus-based cooperative fusion algorithm comprised of coupled observation and localization subsystems is proposed for UAVs to synchronize the target estimation with neighbors’ localization, wherein a fixed-time distributed observer is delicately constructed to provide target speed estimates, such that the requirements on the global availability of target speed can be avoided. The remarkable merit is that without resorting to GPS, all members can reach an agreement on relative positioning estimates in a distributed execution sense. Lyapunov approach certifies that all errors can exponentially approximate to the origin. Simulations confirm the efficacy of the presented algorithm.

IEEE Systems JournalVol. 18(4)
North University of China (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 7%
Target Tracking and Data Fusion in Sensor Networks
3.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.