改进时空DS证据理论在车辆识别中的应用

Yun Lin, Gao Lipeng, Yibing Li, Si Xicai
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引用次数: 1

摘要

本文利用证据理论实现了多传感器、多测量周期的数据融合。讨论了三种核聚变结构:集中式核聚变、无反馈分布式核聚变和有反馈分布式核聚变。在车型识别的应用中,通过理论分析和仿真结果得出,当传感器提供的数据不是很准确(甚至是错误)时,无反馈的分布式融合得到的正确率最高,有反馈的分布式融合次之,集中融合的正确率最差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The application of improving space-time DS evidence theory in distinguishing vehicle
In this paper, it takes advantage of evidence theory to fuse the data with multi-sensors and multi-measuring periods. It discusses three kinds of fusion structures: concentrated fusion, distributed fusion without feedback and distributed fusion with feedback. In the application of vehicle type distinguishing, through theoretical analysis and simulation results, the paper gets the conclusion that when the data provided by the sensors is not very accurate (even wrong), the distributed fusion without feedback can get the highest rate of correct result, the distributed fusion with feedback follows and the concentrated fusion is the worst.
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