• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Performance evaluation of multi-sensor classification systems
【24h】

Performance evaluation of multi-sensor classification systems

机译：多传感器分类系统的性能评估

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A common problem in classification is to use one/more sensors to observe repeated measurements of a target''s features/attributes, and in turn update the targets'' posterior classification probabilities to aid in target identification. This paper addresses the following questions: 1. How do we quantify the classification performance of a sensor? 2. What happens to the posterior probabilities as the number of measurements increase? 3. Will the targets be classified correctly? While the Kalman filter allows for off-line estimation of kinematic performance (covariance matrix), a comparable approach for studying classification accuracy has not been done previously. We develop a new analytical approach for computing the long-run classification performance of a sensor and also present recursive formulas for efficient calculation of the same. We show that, under a minimal condition, a sensor will eventually classify all targets perfectly. We also develop a methodology for evaluating the classification performance of multi-sensor fusion systems involving sensors of varying quality. The contributions of this paper are 1. A simple metric to quantify a sensor''s ability to discriminate between the targets being identified, and its use in comparing multiple sensors, 2. An approximate formula based on this metric to compute off-line estimates of the rate of convergence toward perfect classification, and the number of measurements required to achieve a desired level of classification accuracy, and 3. The use of this metric to evaluate classification performance of multi-sensor fusion systems.
机译：分类中的一个常见问题是使用一个/多个传感器来观察目标的特征/属性的重复测量，然后更新目标的后分类概率以帮助目标识别。本文解决以下问题：1.如何量化传感器的分类性能？ 2.随着测量次数的增加，后验概率会发生什么？ 3.目标分类正确吗？虽然卡尔曼滤波器允许离线评估运动性能（协方差矩阵），但以前尚未进行过类似的研究分类准确性的方法。我们开发了一种用于计算传感器的长期分类性能的新分析方法，并且还提出了用于有效计算该传感器的递归公式。我们表明，在最小条件下，传感器最终将对所有目标进行完美分类。我们还开发了一种方法，用于评估涉及质量不同的传感器的多传感器融合系统的分类性能。本文的贡献是：1.一种简单的度量标准，用于量化传感器区分被识别目标的能力，以及用于比较多个传感器的方法； 2.基于此度量标准的近似公式，用于计算离线估算值达到完美分类的收敛速度，达到期望的分类精度水平所需的测量次数，以及3.使用此度量来评估多传感器融合系统的分类性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号