Phd dissertation on kalman filter

Phd dissertation on kalman filter

Phd Dissertation On Kalman Filter, bucket list items mba essay analysis, write my reaserch paper, hotel rwanda essay ALL PAPERS DOUBLE-CHECKED All homework are Phd Dissertation On Kalman Filter, conclusion words for essays, what size is the font for the title of the essay apa 6th edition, statistics tutor. Affordable rates. First-order discounts. Custom papers that any student can afford. Always Deliver Your Order Before Your Mentioned Deadline We Phd Dissertation On Kalman Filter are prepared to meet Phd Dissertation On Kalman Filter your demands. The geeks are screened based on their resume, qualifications test, and trial assignment. The support managers undergo scenario-based training before day one on the job

Doctoral Dissertations University of Connecticut Graduate School. Dynamic Resource Management Algorithms for Complex Systems and Novel Approaches to Adaptive Kalman Filtering Lingyi Zhang University of Connecticut - Storrs, lingyi. zhang uconn. Recommended Citation Zhang, Lingyi, "Dynamic Resource Management Algorithms for Complex Systems and Novel Approaches to Adaptive Kalman Filtering" Doctoral Dissertations.

Lingyi Zhang, Ph. University of Connecticut, This thesis considers three combinatorial optimization problems of substantial practical importance. First, a new approach to efficiently obtain a large number of ranked solu- tions 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 approxi- mate 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, phd dissertation on kalman filter.

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. Dynamic Resource Management Algorithms for Complex Systems and Novel Approaches to Adaptive Kalman Filtering. A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy at the University of Connecticut.

Thank you to my major advisor, Dr. Krishna Pattipati, for his guidance and patience in molding me into who I am today. It is my utmost honor to work and learn under his guidance and support.

I would like to thank my associate advisor Dr. Yaakov Bar-Shalom, whom I had the pleasure of writing a paper with. He has shaped my presentation and phd dissertation on kalman filter style to be consistent and detail oriented. I also thank Dr. Peter Luh for being on my committee, and whom I had the privilege of being a student in his nonlinear optimization course.

I would like to also particularly thank David Sidoti for being my mentor and aiding my transition to the lab, providing me with valuable support, advice and guidance throughout the years of my graduate school journey.

Lastly, I want to thank my family for their unconditional encouragement, support and love. I would not have made it this far without their support.

Relaxation Method II. Auction Algorithm. Transauction vs. RELAX-IV Algorithm. Appendix A Kalman Filter Derivations A.

Bibliography This dissertation considers two broad topics, one motivated by the need to comply with scarce resource management requirements as defense and industry look to continuously accomplish more with less, and the other motivated by the previous limitations of a steady-state data-driven Kalman filter. The first topic led to the development of efficient dynamic resource management algorithms with applications to nuclear fuel assembly loading pattern optimization, surveillance asset allocation for counter-drug smuggling and multi-objective ship routing, while the second topic resulted in novel approaches for estimating process and measurement noise covariances in adaptive Kalman filtering.

The phd dissertation on kalman filter of automated decision making is to determine and understand the decision context, phd dissertation on kalman filter, and to effectively explore the problem space to present to the Decision Maker DM ranked courses of action to choose from in a timely manner, phd dissertation on kalman filter.

For example, what. The key here is to evaluate only new unique loading patterns assignments. In application to maritime surveillance and drug interdiction, the dynamic resource management problem under uncertainty may be viewed phd dissertation on kalman filter a moving horizon stochastic control problem. In the context of a counter-smuggling mission, the key problem is to efficiently allocate a set of heterogeneous sensing and interdiction assets to maximize the probability of smuggler detection and interdiction, subject to mission constraints, by integrating information, such as intelligence, weather, asset availability, asset capabilities e.

This problem is PSPACE-hard1. In the application involving ship routing, the salient problem is multi-objective planning in a dynamic and uncertain environment. The ship routing problem is exacerbated by the need to address multiple conflicting objectives as many as fifteen objectives, such as fuel efficiency, voyage time, distancespatial and temporal uncertainty associated with the weather and multiple constraints on asset operation e.

Lastly, the second major thrust of this thesis is the identification of noise covariances in a steady-state Kalman filter [85]. The Kalman filter is the state estimator for linear. However, in many practical situations, including noisy feature data in machine learning, the statistics of the noise covariances are often unknown or only partially known. Thus, noise identification is an essential part of adaptive filtering.

Although this problem has a long history, reliable algorithms for their estimation are not available, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this dissertation. This dissertation is phd dissertation on kalman filter as follows: In Chapter 2, we solve the nuclear fuel assembly loading pattern optimization problem where we obtain a large number of ranked solutions as many as ranked solutions to the 3-dimensional 3-D assign- ment problems with a non-unity right-hand side constraint.

Modifications previously phd dissertation on kalman filter in the literature for the 2-dimensional 2-D assignment problem are applied to optimize the search space decomposition for the 3-D assignment problem. In phase II, we solve each subproblem by phd dissertation on kalman filter Lagrangian relaxation and solving the 3-D assignment problem as a combination of relaxed 2-D assignment problems and 2-D transportation problems.

The 2-D assignment problem is solved by the JVC or. The sequence of relaxed 2-D problems are interchangeable, while phd dissertation on kalman filter to the relaxed constraints.

We validate and compare the performance and utility of the proposed algorithms and search space decomposition optimizations via extensive numerical experiments. In Chapter 3, we tackle the problem of targeting in uncertainty, where we delve into surveillance operations in counter-drug smuggling. We validate four approximate dynamic programming approaches and three branch-and-cut-based methods on a maritime surveillance problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to detect multiple drug smugglers using heterogeneous types of transportation on the sea with varying contraband weights.

We validate the proposed algorithmic concepts via realistic mission scenarios. We conduct scalability analyses of the algorithms and conclude that effective asset allocations can be obtained within seconds using rollout-based ADP. The contributions of this work have been transitioned to and are currently being tested by Joint Interagency Task Force—South JIATF- Southan organization tasked with providing the initial line of defense against drug trafficking in the East Pacific and Caribbean Oceans.

Chapter 4 details an enhancement to TMPLAR, a mixed-initiative tool for multi- objective planning and asset routing in dynamic and uncertain environments. It is built upon multi-objective dynamic programming algorithms to route assets in a timely fashion, while considering objectives, such as fuel efficiency, voyage time, distance, and, phd dissertation on kalman filter. The ship routing problem is exacerbated by the need to address multiple conflicting objectives, spatial and temporal uncertainty associated with the weather and multiple constraints on asset operation.

The NAPO algorithm optimizes weather- based objectives in a reasonable amount of time, phd dissertation on kalman filter, optimizing arrival and departure times at waypoints, asset speed and bearing.

The key algorithmic contribution is a fast approximate method for substantially containing the computational complexity by generating the Pareto-front of the multi-objective shortest path problem for networks with stochastic non-convex edge costs, utilizing approximate dynamic programming and clustering techniques. In Chapter 5, we discuss the topic of adaptive Kalman filtering, where we present the new approach to identify the unknown noise covariances.

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown. Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues phd dissertation on kalman filter this thesis. We first present the necessary and sufficient condition for unknown noise covariance estimation; these conditions are related to the rank of a matrix involving the auto and cross- covariances of a weighted sum phd dissertation on kalman filter innovations, where the weights are the coefficients of the minimal polynomial of the closed-loop system transition matrix of a stable, but not necessarily optimal, Kalman filter.

We present an optimization criterion and a novel six-step approach based on a successive approximation, coupled with a gradient algorithm with adaptive step sizes, to estimate the steady-state Kalman filter. Our approach enforces the structural assumptions on unknown noise covariances and ensures symmetry and positive definiteness of the estimated covariance matrices. We provide several approaches to estimate the unknown measurement noise covariance R via post-fit residuals, an approach not yet exploited in the literature.

Phd dissertation on kalman filter validation of the proposed method on five different test cases from the literature demonstrates that the proposed method significantly outperforms previous state-of-the-art methods.

It also offers a number of novel machine learning motivated approaches, such as sequential one sample at a time and mini-batch-based methods, to speed up the computations.

We summarize and discuss the research impact of the proposed approaches in Chapter 6. Journal papers that are accepted and published with phd dissertation on kalman filter authorship include [,]:. Zhang, D, phd dissertation on kalman filter. Sidoti, S. Vallabhaneni, K. Pattipati, and D. Sidoti, phd dissertation on kalman filter, G.

Avvari, D. Ayala, M. Mishra, D. Kellmeyer, J. Hansen, and K. Sidoti, A, phd dissertation on kalman filter. Bienkowski, K. Pattipati, Y. Bar-Shalom, and D. Conference papers that are accepted and published with primary authorship include [, ]:. Sidoti, K. Bienkowski, and K. Zhang, W. Chen, K. Pattipati, A.

Bazzi, S. Joshi, and E. Avvari, M. Mishra, L.

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