Back to results

Massachusetts Institute of Technology

Utilizing I/Q data to enhance radar detection and accuracy metrics

Abstract

dc:description.abstract

The incorporation of advanced digital processing technologies including high-bandwidth networks, low-cost commercial components, and advanced FPGAs into novel radio frequency (RF) sensors has resulted in significantly increased sensor capabilities while at the same time dramatically increasing the size of the data associated with test events. This work focuses on the development of management tools to analyze these large datasets to increase overall situational awareness and as a result, sensor performance which requires the development of advanced algorithms designed to address data decimation, parallelization of processing, and novel detection and filtering techniques among others. These algorithms are developed and optimized through post-processing existing MIT-LL sensor data in MATLAB.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Velez, Alexandria.
Advisor dc:contributor.advisor
  • John N. Tsitsiklis and Josh Erling.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/123125
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/123125

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Velez, Alexandria.. Utilizing I/Q data to enhance radar detection and accuracy metrics. Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123125