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Information-based sensor management for static target detection using real and simulated data.

机译：基于信息的传感器管理，可使用真实和模拟数据进行静态目标检测。

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摘要

In the modern sensing environment, large numbers of sensor tasking decisions must be made using an increasingly diverse and powerful suite of sensors in order to best fulfill mission objectives in the presence of situationally-varying resource constraints. Sensor management algorithms allow the automation of some or all of the sensor tasking process, meaning that sensor management approaches can either assist or replace a human operator as well as ensure the safety of the operator by removing that operator from a dangerous operational environment. Sensor managers also provide improved system performance over unmanaged sensing approaches through the intelligent control of the available sensors. In particular, information-theoretic sensor management approaches have shown promise for providing robust and effective sensor manager performance.;This work develops information-theoretic sensor managers for a general static target detection problem. Two types of sensor managers are developed. The first considers a set of discrete objects, such as anomalies identified by an anomaly detector or grid cells in a gridded region of interest. The second considers a continuous spatial region in which targets may be located at any point in continuous space. In both types of sensor managers, the sensor manager uses a Bayesian, probabilistic framework to model the environment and tasks the sensor suite to make new observations that maximize the expected information gain for the system. The sensor managers are compared to unmanaged sensing approaches using simulated data and using real data from landmine detection and unexploded ordnance (UXO) discrimination applications, and it is demonstrated that the sensor managers consistently outperform the unmanaged approaches, enabling targets to be detected more quickly using the sensor managers. The performance improvement represented by the rapid detection of targets is of crucial importance in many static target detection applications, resulting in higher rates of advance and reduced costs and resource consumption in both military and civilian applications.
机译：在现代传感环境中，必须使用越来越多样化和功能强大的传感器套件来做出大量传感器任务决策，以便在资源随情况变化的情况下最好地实现任务目标。传感器管理算法允许部分或全部传感器任务处理过程自动化，这意味着传感器管理方法可以帮助或替代人工操作员，并且可以通过将操作员从危险的操作环境中撤离来确保操作员的安全。传感器管理器还通过对可用传感器的智能控制，提供了比非托管传感方法更高的系统性能。特别是，信息理论传感器管理方法已显示出提供强大而有效的传感器管理器性能的希望。该工作开发了用于一般静态目标检测问题的信息理论传感器管理器。开发了两种类型的传感器管理器。首先考虑一组离散对象，例如由异常检测器识别的异常或感兴趣的网格区域中的网格单元。第二个考虑了一个连续的空间区域，目标可以位于连续空间中的任何一点。在这两种类型的传感器管理器中，传感器管理器均使用贝叶斯概率框架对环境进行建模，并对传感器套件进行任务分配以进行新观察，以使系统的预期信息收益最大化。将传感器管理器与使用模拟数据以及使用地雷检测和未爆炸弹药（UXO）判别应用程序的真实数据的非管理式传感方法进行比较，并证明传感器管理器始终优于非管理式方法，从而能够使用以下方法更快地检测目标传感器管理器。在许多静态目标检测应用中，以快速检测目标为代表的性能提高至关重要，从而可以提高前进速度并降低军事和民用应用的成本和资源消耗。

著录项

  • 作者

    Kolba, Mark Philip.;

  • 作者单位

    Duke University.;

  • 授予单位 Duke University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 383 p.
  • 总页数 383
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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