Date of Completion
7-14-2020
Embargo Period
7-14-2020
Keywords
Kalman filter, optimization, algorithms, covariance, estimation, noise covariance, approximate dynamic programming, rollout, branch-and-cut
Major Advisor
Krishna R. Pattipati
Associate Advisor
Peter B. Luh
Associate Advisor
Yaakov Bar-Shalom
Field of Study
Electrical Engineering
Degree
Doctor of Philosophy
Open Access
Open Access
Abstract
This thesis considers three combinatorial optimization problems of substantial practical importance. First, a new approach to efficiently obtain a large number of ranked solutions to a 3-dimensional assignment problem is presented, and is applied to generate fuel assembly loading patterns. Second, we formulate the problem of dynamically scheduling maritime surveillance assets, and solve it using branch-and-cut and approximate dynamic programming (ADP) with rollout, and investigate the tradeoffs between the two. Third, a multi-objective ship routing problem is also investigated, where we propose a solution combining approximate dynamic programming techniques and clustering techniques to contain the computational and storage complexity. Lastly, this dissertation develops a seminal approach to adaptive Kalman filtering via the use of post-fit residuals given data samples -- an approach not yet discussed prior to this work.
Recommended Citation
Zhang, Lingyi, "Dynamic Resource Management Algorithms for Complex Systems and Novel Approaches to Adaptive Kalman Filtering" (2020). Doctoral Dissertations. 2564.
https://digitalcommons.lib.uconn.edu/dissertations/2564