{"title":"态势感知度量的交互式分析","authors":"Derek Overby, J. Wall, J. Keyser","doi":"10.1117/12.905187","DOIUrl":null,"url":null,"abstract":"Digital systems are employed to maintain situational awareness of people in various contexts including emergency response, \ndisaster relief, and military operations. Because these systems are often operated in wireless environments and are \nused to support real-time decision making, the accuracy of the SA data provided is important to measure and evaluate in the \ndevelopment of new systems. Our work has been conducted in conjunction with analysts in the evaluation and performance \ncomparison of different systems designed to provide a high degree of situational awareness in military operations. To this \nend, we defined temporal and spatial metrics for measuring the accuracy of the SA data provided by each system. In this \npaper we discuss the proposed temporal and spatial metrics for SA data and show how we provided these metrics in a \nlinked coordinated multiple view environment that enabled the analysts we worked with to effectively perform several critical \nanalysis tasks. The temporal metric is designed to help determine when network performance has a significant effect \non SA data, and therefore identify specific time periods in which individuals were provided inaccurate position data for \ntheir peers. Temporal context can be used to determine the local or global nature of any SA data inaccuracy, and the spatial \nmetric can then be used to identify geographic effects on network performance of the wireless system. We discuss the \ninteractive software implementation of our metrics and show how this analysis capability enabled the analysts to evaluate \nthe observed effects of network latency and system performance on SA data during an exercise.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"58 1","pages":"829406"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Interactive analysis of situational awareness metrics\",\"authors\":\"Derek Overby, J. Wall, J. Keyser\",\"doi\":\"10.1117/12.905187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Digital systems are employed to maintain situational awareness of people in various contexts including emergency response, \\ndisaster relief, and military operations. Because these systems are often operated in wireless environments and are \\nused to support real-time decision making, the accuracy of the SA data provided is important to measure and evaluate in the \\ndevelopment of new systems. Our work has been conducted in conjunction with analysts in the evaluation and performance \\ncomparison of different systems designed to provide a high degree of situational awareness in military operations. To this \\nend, we defined temporal and spatial metrics for measuring the accuracy of the SA data provided by each system. In this \\npaper we discuss the proposed temporal and spatial metrics for SA data and show how we provided these metrics in a \\nlinked coordinated multiple view environment that enabled the analysts we worked with to effectively perform several critical \\nanalysis tasks. The temporal metric is designed to help determine when network performance has a significant effect \\non SA data, and therefore identify specific time periods in which individuals were provided inaccurate position data for \\ntheir peers. Temporal context can be used to determine the local or global nature of any SA data inaccuracy, and the spatial \\nmetric can then be used to identify geographic effects on network performance of the wireless system. We discuss the \\ninteractive software implementation of our metrics and show how this analysis capability enabled the analysts to evaluate \\nthe observed effects of network latency and system performance on SA data during an exercise.\",\"PeriodicalId\":89305,\"journal\":{\"name\":\"Visualization and data analysis\",\"volume\":\"58 1\",\"pages\":\"829406\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Visualization and data analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.905187\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visualization and data analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.905187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interactive analysis of situational awareness metrics
Digital systems are employed to maintain situational awareness of people in various contexts including emergency response,
disaster relief, and military operations. Because these systems are often operated in wireless environments and are
used to support real-time decision making, the accuracy of the SA data provided is important to measure and evaluate in the
development of new systems. Our work has been conducted in conjunction with analysts in the evaluation and performance
comparison of different systems designed to provide a high degree of situational awareness in military operations. To this
end, we defined temporal and spatial metrics for measuring the accuracy of the SA data provided by each system. In this
paper we discuss the proposed temporal and spatial metrics for SA data and show how we provided these metrics in a
linked coordinated multiple view environment that enabled the analysts we worked with to effectively perform several critical
analysis tasks. The temporal metric is designed to help determine when network performance has a significant effect
on SA data, and therefore identify specific time periods in which individuals were provided inaccurate position data for
their peers. Temporal context can be used to determine the local or global nature of any SA data inaccuracy, and the spatial
metric can then be used to identify geographic effects on network performance of the wireless system. We discuss the
interactive software implementation of our metrics and show how this analysis capability enabled the analysts to evaluate
the observed effects of network latency and system performance on SA data during an exercise.