Toward Coordinated Sensor Motion for Classification: An Example of Intrusion Detection Using Bayes Risk.
In this paper we propose a framework for optimal coordinated sensor motion using the Bayes risk. For the purpose of illustration, we address an intrusion detection problem, which is cast as a binary hypothesis testing problem. We consider two distinct hypotheses or classes for moving targets. They are classified as threat or safe, depending on the future target trajectory entering or not entering a specified area of interest. The principal contribution of our work is a formal analysis, under various simplifying assumptions, of how Bayes risk can used to generate sensor motion control laws. We propose the use of the extended Kalman filter (EKF) state estimate and covariance as the summary statistic for the sensor observations. Thus the novelty of our approach lies in combining the classification and estimation problems formally, leading to an optimal coordinated sensor motion control algorithm.
Apoorva Shende Matthew J. Bays Daniel J. Stilwell
Virginia Polytechnic Institute and State University Blacksburg,VA 24060
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
3340-3346
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）