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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2311.14264 |
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| _version_ | 1866916328985591808 |
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| author | Cheng, Xin Han, Guangjie Peng, Jinlin Jiang, Jinfang He, Yu Zhu, Weiqiang Shu, Feng Wang, Jiangzhou |
| author_facet | Cheng, Xin Han, Guangjie Peng, Jinlin Jiang, Jinfang He, Yu Zhu, Weiqiang Shu, Feng Wang, Jiangzhou |
| contents | Deploying multiple unmanned aerial vehicles (UAVs) to locate a signal-emitting source covers a wide range of military and civilian applications like rescue and target tracking. It is well known that the UAVs-source (sensors-target) geometry, namely geometric configuration, significantly affects the final localization accuracy. This paper focuses on the geometric configuration optimization for received signal strength difference (RSSD)-based passive source localization by drone swarm. Different from prior works, this paper considers a general measuring condition where the spread angle of drone swarm centered on the source is constrained. Subject to this constraint, a geometric configuration optimization problem with the aim of maximizing the determinant of Fisher information matrix (FIM) is formulated. After transforming this problem using matrix theory, an alternating direction method of multipliers (ADMM)-based optimization framework is proposed. To solve the subproblems in this framework, two global optimal solutions based on the Von Neumann matrix trace inequality theorem and majorize-minimize (MM) algorithm are proposed respectively. Finally, the effectiveness as well as the practicality of the proposed ADMM-based optimization algorithm are demonstrated by extensive simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_14264 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An ADMM-Based Geometric Configuration Optimization in RSSD-Based Source Localization By UAVs with Spread Angle Constraint Cheng, Xin Han, Guangjie Peng, Jinlin Jiang, Jinfang He, Yu Zhu, Weiqiang Shu, Feng Wang, Jiangzhou Signal Processing Deploying multiple unmanned aerial vehicles (UAVs) to locate a signal-emitting source covers a wide range of military and civilian applications like rescue and target tracking. It is well known that the UAVs-source (sensors-target) geometry, namely geometric configuration, significantly affects the final localization accuracy. This paper focuses on the geometric configuration optimization for received signal strength difference (RSSD)-based passive source localization by drone swarm. Different from prior works, this paper considers a general measuring condition where the spread angle of drone swarm centered on the source is constrained. Subject to this constraint, a geometric configuration optimization problem with the aim of maximizing the determinant of Fisher information matrix (FIM) is formulated. After transforming this problem using matrix theory, an alternating direction method of multipliers (ADMM)-based optimization framework is proposed. To solve the subproblems in this framework, two global optimal solutions based on the Von Neumann matrix trace inequality theorem and majorize-minimize (MM) algorithm are proposed respectively. Finally, the effectiveness as well as the practicality of the proposed ADMM-based optimization algorithm are demonstrated by extensive simulations. |
| title | An ADMM-Based Geometric Configuration Optimization in RSSD-Based Source Localization By UAVs with Spread Angle Constraint |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2311.14264 |