Year: 2007
Type: dissertation
Abstract: Sensor management may be defined as those stochastic control problems in which control values are selected to influence sensing parameters in order to maximize the utility of the resulting measurements for an underlying detection or estimation problem.While problems of this type can be formulated as a dynamic program, the state space of the program is in general infinite, and traditional solution techniques are inapplicable.Despite this fact, many authors have applied simple heuristics such as greedy or myopic controllers with great success.This thesis studies sensor management problems in which information theoretic quantities such as entropy are utilized to measure detection or estimation performance.The work has two emphases: firstly, we seek performance bounds which guarantee performance of the greedy heuristic and derivatives thereof in certain classes of problems.Secondly, we seek to extend these basic heuristic controllers to find algorithms that provide improved performance and are applicable in larger classes of problems for which the performance bounds do not apply.The primary problem of interest is multiple object tracking and identification; application areas include sensor network management and multifunction radar control.Utilizing the property of submodularity, as proposed for related problems by different authors, we show that the greedy heuristic applied to sequential selection problems with information theoretic objectives is guaranteed to achieve at least half of the optimal reward.Tighter guarantees are obtained for diffusive problems and for problems involving discounted rewards.Online computable guarantees also provide tighter bounds in specific problems.The basic result applies to open loop selections, where all decisions are made before any observation values are received; we also show that the closed loop C.S. Lewis It has been a wonderful privilege to have been able to study under and alongside such a tremendous group of people in this institution over the past few years.There are many people whom I must thank for making this opportunity the great experience that it has been.Firstly, I offer my sincerest thanks to my advisors, Dr John Fisher III and Prof Alan Willsky, whose support, counsel and encouragement has guided me through these years.The Army Research Office, the MIT Lincoln Laboratory Advanced Concepts Committee and the Air Force Office of Scientific Research all supported this research at various stages
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Army Research Office
OpenAlex ID: W2105357975