FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV Swarms
To achieve precise collaborative localization of multiple unmanned aerial vehicles (UAVs) in hardware environments, this paper presents an field-programmable gate array-based particle swarm optimization (PSO) algorithm aimed at enhancing the localization efficiency of multiple nodes targeting a spec...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/8/2462 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850180601250840576 |
|---|---|
| author | Chuanhao Zhang Changsheng Li Zhipeng Chen Haojie Li Hang Yu |
| author_facet | Chuanhao Zhang Changsheng Li Zhipeng Chen Haojie Li Hang Yu |
| author_sort | Chuanhao Zhang |
| collection | DOAJ |
| description | To achieve precise collaborative localization of multiple unmanned aerial vehicles (UAVs) in hardware environments, this paper presents an field-programmable gate array-based particle swarm optimization (PSO) algorithm aimed at enhancing the localization efficiency of multiple nodes targeting a specific object. By leveraging the unique computational capabilities of FPGA, the proposed algorithm integrates optimization strategies, including particle mutation, variable crossover probabilities, and adjustable weights. These strategies collectively enhance the performance of the PSO algorithm in localization tasks. Comparative simulations conducted across a range of operational scenarios demonstrate that the algorithm not only ensures high localization accuracy but also delivers excellent real-time performance and rapid convergence. To further validate the algorithm’s practical applicability, a four-node collaborative localization platform was developed, and experiments were carried out. The results confirmed the feasibility of multi-node collaborative localization, underscoring the advantages of the proposed algorithm, such as high accuracy, fast convergence, and robust stability. |
| format | Article |
| id | doaj-art-c7fe455e03ef496d8706d5fc279c4f29 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c7fe455e03ef496d8706d5fc279c4f292025-08-20T02:18:05ZengMDPI AGSensors1424-82202025-04-01258246210.3390/s25082462FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV SwarmsChuanhao Zhang0Changsheng Li1Zhipeng Chen2Haojie Li3Hang Yu4School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaTo achieve precise collaborative localization of multiple unmanned aerial vehicles (UAVs) in hardware environments, this paper presents an field-programmable gate array-based particle swarm optimization (PSO) algorithm aimed at enhancing the localization efficiency of multiple nodes targeting a specific object. By leveraging the unique computational capabilities of FPGA, the proposed algorithm integrates optimization strategies, including particle mutation, variable crossover probabilities, and adjustable weights. These strategies collectively enhance the performance of the PSO algorithm in localization tasks. Comparative simulations conducted across a range of operational scenarios demonstrate that the algorithm not only ensures high localization accuracy but also delivers excellent real-time performance and rapid convergence. To further validate the algorithm’s practical applicability, a four-node collaborative localization platform was developed, and experiments were carried out. The results confirmed the feasibility of multi-node collaborative localization, underscoring the advantages of the proposed algorithm, such as high accuracy, fast convergence, and robust stability.https://www.mdpi.com/1424-8220/25/8/2462cooperative target localizationparticle swarm optimization algorithmFPGA deployment and verificationalgorithm localization capability analysis |
| spellingShingle | Chuanhao Zhang Changsheng Li Zhipeng Chen Haojie Li Hang Yu FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV Swarms Sensors cooperative target localization particle swarm optimization algorithm FPGA deployment and verification algorithm localization capability analysis |
| title | FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV Swarms |
| title_full | FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV Swarms |
| title_fullStr | FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV Swarms |
| title_full_unstemmed | FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV Swarms |
| title_short | FPGA-Based Particle Swarm Collaborative Target Localization Algorithm for UAV Swarms |
| title_sort | fpga based particle swarm collaborative target localization algorithm for uav swarms |
| topic | cooperative target localization particle swarm optimization algorithm FPGA deployment and verification algorithm localization capability analysis |
| url | https://www.mdpi.com/1424-8220/25/8/2462 |
| work_keys_str_mv | AT chuanhaozhang fpgabasedparticleswarmcollaborativetargetlocalizationalgorithmforuavswarms AT changshengli fpgabasedparticleswarmcollaborativetargetlocalizationalgorithmforuavswarms AT zhipengchen fpgabasedparticleswarmcollaborativetargetlocalizationalgorithmforuavswarms AT haojieli fpgabasedparticleswarmcollaborativetargetlocalizationalgorithmforuavswarms AT hangyu fpgabasedparticleswarmcollaborativetargetlocalizationalgorithmforuavswarms |