| Autor: |
Hao Wu, Peng-fei Wu, Zhang-song Shi, Shi-yan Sun, Zhong-hong Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Defence Technology, Vol 25, Iss , Pp 231-248 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2214-9147 |
| DOI: |
10.1016/j.dt.2022.04.014 |
| Popis: |
In the internet of battlefield things, ammunition is becoming networked and intelligent, which depends on location information. Therefore, this paper focuses on the self-organized network collaborative localization of munitions with an aerial three-dimensional (3D) highly-dynamic topographic structure under a satellite denied environment. As for aerial networked munitions, the measurement of munitions is objectively incomplete due to the degenerated and interrupted link of munitions. For this reason, a cluster-oriented collaborative localization method is put forward in this paper. Multidimensional scaling (MDS) was first integrated with a trilateration localization method (TLM) to construct a relative localization algorithm for determining the relative location of a mobile cluster network. The information related to relative velocity was then combined into a collaborative localization framework to devise a TLM-vMDS algorithm. Finally, an iterative refinement algorithm based on scaling by majorizing a complicated function (SMACOF) was employed to effectively eliminate the influence of incomplete link observation on localization accuracy. Compared with the currently available advanced algorithms, the proposed TLM-vMDS algorithm achieves higher localization accuracy and faster convergence for a cluster of extensively networked munitions, and also offers better numerical stability and robustness for high-speed motion models. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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