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Paper TuB05.4

Huang, Shuo (Michigan Tech. University), Tan, Jindong (Michigan Technological University)

Adaptive Sampling Using Mobile Sensor Networks

Scheduled for presentation during the Regular Session "Sensor Networks" (TuB05), Tuesday, May 15, 2012, 11:15−11:30, Meeting Room 5 (Ska)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on February 21, 2018

Keywords Robust/Adaptive Control of Robotic Systems, Networked Robots

Abstract

This paper presents an adaptive sparse sampling approach and the corresponding real-time scalar field reconstruction method using mobile sensor networks. Traditionally, the sampling methods collect measurements without considering possible distributions of target signals. A feedback driven algorithm is discussed in this paper, where new measurements are determined based on the analysis of existing observations. The information amount of each potential measurement is evaluated under a sparse domain based on compressive sensing framework given all existing information shared among networked mobile sensors, and the most informative one is selected. The efficiency of this information-driven method falls into the information maximization for each individual measurement. The simulation results show the efficacy and efficiency of this approach, where a scalar field is recovered.

 

 

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