國立虎尾科技大學 |

Machine Learning Enhanced Antenna Systems.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning Enhanced Antenna Systems./
作者:	Friedrichs, Gaeron R.
面頁冊數:	1 online resource (176 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798379529789

Machine Learning Enhanced Antenna Systems.
Friedrichs, Gaeron R.

Machine Learning Enhanced Antenna Systems. - 1 online resource (176 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2023.

Includes bibliographical references

Radio direction finding (DF) has long since been of interest to the antenna and greater RF community. DF helps address problems seen in both commercial and defense applications, and the needs are ever-growing. A substantial breadth of research addresses maximizing the performance of DF systems. High quality antenna design, high performance beamforming, and high resolution processing have been demonstrated with great success, though often sacrificing size, weight, power, or cost (SWAP-C). The widespread proliferation of the Internet of Things and generally increased connectivity opens up research areas that leverage unprecedented levels of networking and connectivity. These systems require low-cost, low-fidelity RF components to enable mass proliferation, which makes engineering well-performing solutions difficult by comparison to historically specialized sensors. This thesis seeks to provide low-cost sensing solutions, leveraging machine learning and modern processing techniques, that yield high performance and are compatible with modern day edge-computing requirements.An investigation into the feasibility of using machine learning for retrofitting - the extraction of information for which a system is not conventionally designed for - is explored in the context of polarimetry. Fundamental studies are used to demonstrate the practicality of machine learning to facilitate low-cost sensing capabilities. An ultra-wideband log periodic antenna is first designed for additive manufacturing to integrate the antenna and feeding structure (balun and impedance transformer) into the body of the sensor itself. The dual-polarized system enables wideband spectrum sensing at a relatively low cost. This system, in isolation, does not sense angle-of-arrival information, therefore antenna-induced distortions imparted on the signal cannot be corrected with DF-based calibration methods, making polarimetry difficult. A neural network solution is demonstrated which facilitates good accuracy in classifying the polarization of the incident signal, whereas polarimetry is otherwise unachievable.An extension of the retrofitting motif is further explored through the lens of DF. An ultrawideband circular antenna array is investigated for its ability to perform amplitude-only DF, despite its radiation patterns being suboptimal for the task. Machine learning is once again utilized, but with careful consideration to the practical deployment aspects of the algorithm. A novel, general, architecture is developed to leverage the rotational symmetry of the uniform circular array to perform single-snapshot azimuth angle-of-arrival estimation with as minimal a footprint as possible. The architecture scales favorably, and its relative performance only increases as a function of the number of elements, when compared to competing methods. An elevation sensing capability is also shown with the same sensor, which is unprecedented for other systems with similar field-of-view (FOV) and bandwidth. These concepts are then extended to the design of a structure. The design of as low-cost, low-complexity system is achieved, while maintaining azimuth and elevation DF capabilities with a single snapshot. Fundamental studies on the number of elements and element geometries are carried out, and a particular geometry is built and tested with a custom, four-channel receiver. A systematic neural network design methodology is introduced to facilitate the construction of neural networks for DF. Experimental validation is performed showing the efficacy of integrating machine learning for azimuth and elevation estimation, with a low-element count antenna array and receiver.Finally, the synthesis of all the prior investigations is achieved. Novel use of a four-arm spiral antenna sensor is proposed, using each arm of the spiral as its own amplitude-only channel. Traditionally, without mode-forming, DF is quite difficult. Frequency rotation modeling is deployed alongside a compact neural network architecture to facilitate ultrawideband amplitude-only DF in both azimuth and elevation with a cavity backed spiral in the absence of mode/beam-forming circuitry that is conventionally required. Integration with a four-channel receiver is demonstrated and performance over multiple octaves is obtained.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379529789Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

AntennaIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning Enhanced Antenna Systems.
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