Title:  Motion Tomography Performed by Underwater Mobile Sensor Networks

Committee:

Dr. Fumin Zhang, ECE, Chair , Advisor

Dr. Catherine Edwards, Skidaway Institute of Oceanography, Co-Advisor

Dr. Ayanna Howard, ECE

Dr. Patricio Vela, ECE

Dr. WenZhan Song, GSU

Dr. Yang Wang, CEE

Abstract:

Knowledge of a flow field is crucial to guide underwater mobile sensing agents (UMSAs). However, existing regional ocean models provide insufficient spatial and temporal resolutions for precise guidance of UMSAs. Because of the imperfect knowledge of flow, the actual trajectory of a UMSA deviates from the predicted trajectory, providing Lagrangian information of the underlying flow field. This thesis develops a method referred to as motion tomography (MT) for creating a high-resolution flow map in the region traversed by UMSAs. The MT method formulates an inverse problem based on the collective trajectory information of UMSAs which is affected by the underlying flow field and solve this inverse problem to estimate the underlying flow field. To solve such inverse problem, an iterative sub-optimization method extended from the Kaczmarz method is developed. The MT method is first analyzed for time-invariant flow fields with both non-parametric and parametric flow. Then, to resolve the coupling between temporal variations and spatial variations in flow modeling, two approaches are proposed. First, both Eulerian data collected from stationary buoys and Lagrangian data collected from UMSAs are incorporated into a data-driven flow model, leading parameter estimation for the data-driven flow model to a nonlinear filtering problem. To estimate the parameters of the flow model, this nonlinear filtering problem is decoupled into two linear sub-filtering problems for temporal and spatial parameter estimation, respectively. Observability for both temporal and spatial parameter estimation is analyzed. The second approach discretizes the MT mapping domain in both space and time, which may cause the solution variable to be high dimension and sparse. By employing parametric flow model, spatial and temporal parameters are separately and iteratively estimated through MT. The trajectory information of UMSAs may not be gathered collectively. To deal such case, the iterative sub-optimization method for MT is extended to a distributed version. Based on the distributed sub-optimization method, distributed MT is developed, in which an inverse problem for MT is solved in a distributed fashion. The proposed methods are demonstrated through either simulations and experiments.