Adaptive sampling and forecasting with mobile sensor networks

This thesis addresses planning of mobile sensor networks to extract the best information possible out of the environment to improve the (ensemble) forecast at some verification region in the future. To define the information reward associated with sensing paths, the mutual information is adopted to represent the influence of the measurement actions on the reduction of the uncertainty in the verification variables. The sensor networks planning problems are posed in both discrete and continuous time/space, each of which represents a different level of abstraction of the decision space. In the discrete setting, the targeting problem is formulated to determine the sequence of information-rich waypoints for mobile sensors. A backward formulation is developed to efficiently quantify the information rewards in this combinatorial decision process. This approach computes the reward of each possible sensing choice by propagating the information backwards from the verification time/space to the search space/time. It is shown that this backward method provides an equivalent solution to a standard forward approach, while only requiring the calculation of a single covariance update. This work proves that the backward approach works significantly faster than the forward approach for the ensemble-based representation. In the continuous setting, the motion planning problem that finds the best steering commands of the sensor platforms is posed. The main difficulty in this continuous decision lies in the quantification the mutual information between the future verification variables and a continuous history of the measurement.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Choi, Han-Lim

Advisor dc:contributor.advisor

• Jonathan P. How.

• Aeronautics and Astronautics.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/47790

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/47790