{"title":"Evaluation of the Monitoring Capabilities of Remote Sensing Satellites for Maritime Moving Targets","authors":"Weiming Li, Zhiqiang Du, Li Wang, Tiancheng Zhou","doi":"10.3390/ijgi13090325","DOIUrl":null,"url":null,"abstract":"Although an Automatic Identification System (AIS) can be used to monitor trajectories, it has become a reality for remote sensing satellite clusters to monitor maritime moving targets. The increasing demand for monitoring poses challenges for the construction of satellites, the monitoring capabilities of which urgently need to be evaluated. Conventional evaluation methods focus on the spatial characteristics of monitoring; however, the temporal characteristics and the target’s kinematic characteristics are neglected. In this study, an evaluation method that integrates the spatial and temporal characteristics of monitoring along with the target’s kinematic characteristics is proposed. Firstly, a target motion prediction model for calculating the transfer probability and a satellite observation information calculation model for obtaining observation strips and time windows are established. Secondly, an index system is established, including the target detection capability, observation coverage capability, proportion of empty window, dispersion of observation window, and deviation of observation window. Thirdly, a comprehensive evaluation is completed through combining the analytic hierarchy process and entropy weight method to obtain the monitoring capability score. Finally, simulation experiments are conducted to evaluate the monitoring capabilities of satellites for ship trajectories. The results show that the method is effective when the grid size is between 1.6 and 1.8 times the target size and the task duration is approximately twice the time interval between trajectory points. Furthermore, the method is proven to be usable in various environments.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/ijgi13090325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Although an Automatic Identification System (AIS) can be used to monitor trajectories, it has become a reality for remote sensing satellite clusters to monitor maritime moving targets. The increasing demand for monitoring poses challenges for the construction of satellites, the monitoring capabilities of which urgently need to be evaluated. Conventional evaluation methods focus on the spatial characteristics of monitoring; however, the temporal characteristics and the target’s kinematic characteristics are neglected. In this study, an evaluation method that integrates the spatial and temporal characteristics of monitoring along with the target’s kinematic characteristics is proposed. Firstly, a target motion prediction model for calculating the transfer probability and a satellite observation information calculation model for obtaining observation strips and time windows are established. Secondly, an index system is established, including the target detection capability, observation coverage capability, proportion of empty window, dispersion of observation window, and deviation of observation window. Thirdly, a comprehensive evaluation is completed through combining the analytic hierarchy process and entropy weight method to obtain the monitoring capability score. Finally, simulation experiments are conducted to evaluate the monitoring capabilities of satellites for ship trajectories. The results show that the method is effective when the grid size is between 1.6 and 1.8 times the target size and the task duration is approximately twice the time interval between trajectory points. Furthermore, the method is proven to be usable in various environments.