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Paper WeA212.1

Shende, Apoorva (Virginia Tech), Bays, Matthew (Virginia Tech), Stilwell, Daniel (Virginia Tech)

Toward Coordinated Sensor Motion for Classification: An Example of Intrusion Detection Using Bayes Risk

Scheduled for presentation during the Regular Sessions "Cooperative Control for Multiple Robots" (WeA212), Wednesday, May 11, 2011, 10:05−10:20, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords Path Planning for Multiple Mobile Robots or Agents, Intrusion Detection, Identification and Security, Distributed Robot Systems

Abstract

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.

 

 

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