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Adam Mitchell Dissertation Final.pdf (17.33 MB)

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Advancements and Applications of the Fully Adaptive Radar Framework

Mitchell, Adam E
http://rave.ohiolink.edu/etdc/view?acc_num=osu1523891341711601

Abstract Details

2018, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
To more intelligently interact with their environments, cognitive radars draw inspiration from how biological systems sense, learn, and adapt to their surroundings. The dynamic feedback loop between the cognitive system and the environment is defined as the perception-action cycle. The fully adaptive radar (FAR) framework provides a general structure for emulating the cognitive perception-action cycle in radar systems. In this work, new refinements, extensions, and applications of the FAR framework are proposed. A new construction of the FAR framework is proposed to better align with the structures theorized in cognitive neuropsychology. In this new format, the FAR cost function development, which was previously only vaguely discussed, can be well described using general functions inspired by multi-objective optimization theory. An example of cost function creation is provided. Both experimental and simulated results demonstrate how the user can quickly tailor the system performance for specific tasks by selecting the parameters of a general cost function which reflect their goals. Next, the FAR framework is applied to the task of adaptive radar imaging. This represents an important demonstration because applications of the FAR framework had previously focused exclusively on target tracking. The proposed system adapts the image resolution based on its perceptions of the scene, and it successfully operated both on a simulated scene and the post-processed measurements from the GOTCHA dataset. Finally, the FAR framework is extended to a hierarchical construction. This hierarchical fully adaptive radar (HFAR) framework allows multiple perception-action cycles to be connected, producing increasingly abstract perceptions and actions further up the hierarchy. The HFAR framework allows complex problems of varying scales to be implemented using an internally consistent structure. As an example of this approach, a two-tiered hierarchy for sensor fusion and target tracking is detailed. The efficacy of this proposed system is demonstrated through real-time experiments.
Graeme Smith (Advisor)
Fernando Teixeira (Committee Member)
Lee Potter (Committee Member)
Stuart Ludsin (Committee Member)
174 p.
Electrical Engineering
Fully adaptive radar; Cognitive radar;

Recommended Citations

Citations

  • Mitchell, A. E. (2018). Advancements and Applications of the Fully Adaptive Radar Framework [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523891341711601

    APA Style (7th edition)

  • Mitchell, Adam. Advancements and Applications of the Fully Adaptive Radar Framework. 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1523891341711601.

    MLA Style (8th edition)

  • Mitchell, Adam. "Advancements and Applications of the Fully Adaptive Radar Framework." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523891341711601

    Chicago Manual of Style (17th edition)

osu1523891341711601
364
© 2018, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.