Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm
The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/12/2944 |
| _version_ | 1827656900688216064 |
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| author | Yongkun Zhou Dan Song Bowen Ding Bin Rao Man Su Wei Wang |
| author_facet | Yongkun Zhou Dan Song Bowen Ding Bin Rao Man Su Wei Wang |
| author_sort | Yongkun Zhou |
| collection | DOAJ |
| description | The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise. |
| first_indexed | 2024-03-09T22:35:47Z |
| format | Article |
| id | doaj.art-6fe80631a89f448ba4a9822a375a9230 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T22:35:47Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6fe80631a89f448ba4a9822a375a92302023-11-23T18:49:17ZengMDPI AGRemote Sensing2072-42922022-06-011412294410.3390/rs14122944Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV SwarmYongkun Zhou0Dan Song1Bowen Ding2Bin Rao3Man Su4Wei Wang5The School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518000, ChinaThe Colloege of Information and Communication, National Uinversity of Defense Technology, Xi’an 710006, ChinaThe School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518000, ChinaThe School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518000, ChinaThe Beijing Institute of Tracking and Telecommunication Technology, Beijing 100080, ChinaThe School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518000, ChinaThe problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.https://www.mdpi.com/2072-4292/14/12/2944passive localizationunmanned aerial vehicle (UAV) swarmant colony pheromone mechanism |
| spellingShingle | Yongkun Zhou Dan Song Bowen Ding Bin Rao Man Su Wei Wang Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm Remote Sensing passive localization unmanned aerial vehicle (UAV) swarm ant colony pheromone mechanism |
| title | Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm |
| title_full | Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm |
| title_fullStr | Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm |
| title_full_unstemmed | Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm |
| title_short | Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm |
| title_sort | ant colony pheromone mechanism based passive localization using uav swarm |
| topic | passive localization unmanned aerial vehicle (UAV) swarm ant colony pheromone mechanism |
| url | https://www.mdpi.com/2072-4292/14/12/2944 |
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