University of Kansas
Spectral Cohabitation and Interference Mitigation via Physical Radar Emissions
Abstract
dc:description.abstractAuctioning of frequency bands to support growing demand for high bandwidth 5G communications is driving research into spectral cohabitation strategies for next generation radar systems. The loss of radio frequency (RF) spectrum once designated for radar operation is forcing radar systems to either learn how to coexist in these frequency spectrum bands, without causing mutual interference, or move to other bands of the spectrum, the latter being the more undesirable choice. Two methods of spectral cohabitation are proposed and presented in this work, each taking advantage of recent developments in random frequency modulation (RFM) waveforms, which have the advantage of never repeating. RFM waveforms are optimized to have favorable radar waveform properties while also readily incorporating agile spectral notches. The first method of spectral cohabitation uses these spectral notches to avoid narrow-band RF interference (RFI) in the form of other spectrum users residing in the same band as the radar system, allowing both to operate while minimizing mutual interference. The second method of spectral cohabitation uses an optimization procedure to embed a communications signal into a dual-purpose radar/communications emission, thus allowing one waveform to serve both functions simultaneously. Both of these methods are presented and described in detail as well as being validated through simulation and physical open-air experimentation.
Degree
thesis:*- Grantor dc:publisher
- University of Kansas
- Year dc:date.issued
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Ravenscroft, Gerald Brandon
- Advisor dc:contributor.advisor
-
- Blunt, Shannon D
Subjects
dc:subject × 4Rights
dc:rights- Statement dc:rights
-
- Copyright held by the author.
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Dc Identifier Other
- http://dissertations.umi.com/ku:18946
- OAI identifier oai:identifier
- oai:kuscholarworks.ku.edu:1808/35030