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Search Results (3)

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Keywords = bearing-only UAV networks

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21 pages, 2588 KB  
Article
Distributed Consensus-Based Tracking with Inverse Covariance Intersection in Bearing-Only UAV Networks
by Guangyu Yang, Wenhui Ma, Wenxing Fu, Supeng Zhu and Tong Zhang
Drones 2026, 10(2), 107; https://doi.org/10.3390/drones10020107 - 2 Feb 2026
Abstract
High-precision and consensus tracking of a long-range maneuvering target presents a significant challenge for unmanned aerial vehicles (UAVs) in complex denied environments. Earlier studies rarely considered the fast convergence and fusion accuracy of distributed consensus tracking in bearing-only UAV networks. This article proposes [...] Read more.
High-precision and consensus tracking of a long-range maneuvering target presents a significant challenge for unmanned aerial vehicles (UAVs) in complex denied environments. Earlier studies rarely considered the fast convergence and fusion accuracy of distributed consensus tracking in bearing-only UAV networks. This article proposes a distributed consensus-based estimation (DCE) method with inverse covariance intersection (ICI) fusion rule in the framework of local estimation, consensus iteration, and fusion estimation. Combined with the contribution of measurements from neighboring UAVs, the local estimation of target tracking can be achieved by a square-root cubature information filter (SRCIF) in bearing-only UAVs. Based on local estimation and centralities in a multi-UAV network, each UAV platform can obtain consensus results in a finite number of iterations. Then, the fusion estimations are the consensus with the global ICI fusion rule. Furthermore, the fusion estimations are analyzed in consensus, finiteness, and boundedness. Numerical simulations are performed to validate the effectiveness and superiority of the proposed DCE–ICI method. Full article
(This article belongs to the Section Drone Communications)
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24 pages, 2581 KB  
Article
Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles
by Edward-Joseph Cefai, Matthew Coombes and Daniel O’Boy
Sensors 2025, 25(2), 402; https://doi.org/10.3390/s25020402 - 11 Jan 2025
Cited by 1 | Viewed by 1461
Abstract
Sensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sensor clusters, [...] Read more.
Sensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sensor clusters, which result in the collection of redundant information, which can deteriorate performance while also increasing the associated network costs. Therefore, this work combines a predictive posterior distribution methodology with a novel simplified objective function for optimally identifying and forming smaller sensor clusters before activation and measurement collection. The goal of the proposed objective function is to reduce network communication and computation costs while still maintaining the tracking performance of using far more sensors. The developed optimisation algorithm results in reducing the size of selected sensor clusters by an average of 50% while still maintaining the tracking performance of general traditional techniques. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Wireless Sensor Networks and the Internet of Things)
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19 pages, 4917 KB  
Article
Target Tracking and Circumnavigation Control for Multi-Unmanned Aerial Vehicle Systems Using Bearing Measurements
by Zican Zhou, Jiangping Hu, Bo Chen, Xixi Shen and Bin Meng
Actuators 2024, 13(9), 323; https://doi.org/10.3390/act13090323 - 25 Aug 2024
Cited by 6 | Viewed by 2194
Abstract
This paper addresses the problem of target tracking and circumnavigation control for a bearing-only multi-Unmanned Aerial Vehicle (UAV) system. First, using the bearing measurements, an adaptive algorithm in the form of a Proportional Integral (PI) controller is developed to estimate the target state. [...] Read more.
This paper addresses the problem of target tracking and circumnavigation control for a bearing-only multi-Unmanned Aerial Vehicle (UAV) system. First, using the bearing measurements, an adaptive algorithm in the form of a Proportional Integral (PI) controller is developed to estimate the target state. Subsequently, a distributed circumnavigation control protocol is established to evenly encircle the target. Then, we use the local information from each UAV in the network to calculate the relative position of the target, and further enhance the accuracy of estimation and circumnavigation algorithms by employing a Kalman filter. Finally, numerical simulation experiments are conducted to validate the effectiveness of the proposed tracking control algorithm. Full article
(This article belongs to the Special Issue Design, Modeling, and Control of UAV Systems)
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