| Autor: |
Xiaojun Mei, Dezhi Han, Yanzhen Chen, Huafeng Wu, Teng Ma |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Alexandria Engineering Journal, Vol 68, Iss , Pp 227-238 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1110-0168 |
| DOI: |
10.1016/j.aej.2023.01.028 |
| Popis: |
Marine search and rescue (MSR) is considered the last line of defense for human life at sea. Recently, a prospective MSR strategy based on wireless sensor networks (WSNs) has been developed, and distress-stricken individuals can be located utilizing various localization methods. Nevertheless, the accuracy cannot satisfy the requirement of related departments, especially when employing a single measurement localization technique, such as received signal strength (RSS)-based technology, in a dynamic and complicated ocean environment. To this end, a scheme inspired by information fusion is developed, which incorporates RSS and time of arrival (TOA) information. The maximum likelihood (ML)-based localization problem is then converted into a hybrid measurement alternative nonnegative constrained least squares (HM-ANCLS) framework. Moreover, the paper develops a two-step linearization localization approach (TLLA) to determine the target location. The first step proposes a slight computation method (SCM) that relies on an active set approach to address the framework. In the second step, the paper presents an error correction approach based on the first-order Taylor series expansion to refine the solution. In addition, the paper conducts the Cramér-Rao low bound (CRLB) and the computational complexity of the hybrid scheme. Simulations reveal that TLLA outperforms other state-of-the-art approaches in various situations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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