In this dissertation, I introduce modularization as a means of efficiently solving problems represented by dynamic Bayesian networks and study the properties and effects of modularization relative to traditional solutions. Modularizing a Bayesian filter allows its results to be calculated faster than a traditional Bayesian filter. Traditional Bayesian filters can have issues when large problems must be solved within a short period of time. Modularization addresses this issue by dividing the full problem into a set of smaller problems that can then be solved with separate Bayesian filters. Since the time complexity of Bayesian filters is greater than linear, solving several smaller problems is cheaper than solving a single large problem. The cost of reassembling the results from the smaller problems is comparable to the cost of the smaller problems. This document introduces the concept of both exact and approximate modular Bayesian filters and describes how to design each of the elements of a modular Bayesian filters. These concepts are clarified by using a series of examples from the realm of vehicle state estimation and include the results of each stage of the algorithm creation in a simulated environment. A final section shows the implementation of a modular Bayesian filter in a real-world problem tasked with addressing the problem of vehicle state estimation in the face of transitory sensor failure. This section also includes all of the attending algorithms that allow the problem to be solved accurately and in real-time.
|Advisor:||Maida, Anthony S.|
|Commitee:||Chu, Chee-Hung H., Lakhotia, Arun, Raghavan, Vijay V.|
|School:||University of Louisiana at Lafayette|
|School Location:||United States -- Louisiana|
|Source:||DAI-B 76/11(E), Dissertation Abstracts International|
|Subjects:||Artificial intelligence, Computer science|
|Keywords:||Bayesian filters, Dynamic Bayesian networks, Gaussian filters, Kalman filter, Non-parametric filters, Particle filters|
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