MAXIMAL-LENGTH SEQUENCE CODE CLASSIFICATION OPTIMIZATION PROCEDURE UTILIZING DEEP LEARNING NEURAL NETWORKS
| dc.contributor.advisor | Romero, Ric | |
| dc.contributor.author | Reeder, Christina N. | |
| dc.contributor.department | Electrical and Computer Engineering (ECE) | |
| dc.contributor.secondreader | Cristi, Roberto | |
| dc.date.accessioned | 2024-07-11T18:41:52Z | |
| dc.date.available | 2024-07-11T18:41:52Z | |
| dc.date.issued | 2022-09 | |
| dc.description.abstract | Direct sequence spread spectrum techniques are often utilized in encoding communications signals because they can decrease signal spectrum lower than the thermal noise floor of a receiver, making them harder to detect. Accurate and timely classification of spreading codes for message decoding has become an area of interest. In this work, we evaluate the difference in classification performance between a traditional matched filter bank method and trained neural networks. We demonstrate that trained neural networks may out perform matched filters specifically in the medium SNR range. Additionally, we explore performance of a neural network trained to detect and classify direct sequence coded signals along with a null alternative by adding a “noise only” signal classification option. We find that there is a probability of false alarm (𝑃𝑓a) associated with a neural network trained to detect signals with a “noise only” classification option. We conclude that trained neural networks offer an increase in both percentage of classification (𝑃𝑐) and time-to-classify performance. However, we also conclude that more work is required to optimize the neural network for the decoding of preamble codes of different lengths and types. This work demonstrates the feasibility of using trained neural networks for use in decoding direct sequence coded signals. | |
| dc.description.distributionstatement | Distribution Statement A. Approved for public release: Distribution is unlimited. | |
| dc.description.service | Lieutenant, United States Navy | |
| dc.description.sponsorship | NAVAIR, Patuxent River, MD 20670 | |
| dc.identifier.curriculumcode | 590, Electronic Systems Engineering | |
| dc.identifier.thesisid | 37020 | |
| dc.identifier.uri | https://hdl.handle.net/10945/73049 | |
| dc.publisher | Monterey, CA; Naval Postgraduate School | |
| dc.rights | This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States. | |
| dc.subject.author | machine learning | |
| dc.subject.author | signal detection | |
| dc.subject.author | signal classification | |
| dc.subject.author | maximal-length sequence | |
| dc.subject.author | spread spectrum | |
| dc.subject.author | deep learning | |
| dc.subject.author | neural network | |
| dc.title | MAXIMAL-LENGTH SEQUENCE CODE CLASSIFICATION OPTIMIZATION PROCEDURE UTILIZING DEEP LEARNING NEURAL NETWORKS | |
| dc.type | Thesis | |
| dspace.entity.type | Publication | |
| etd.thesisdegree.discipline | Electrical Engineering | |
| etd.thesisdegree.grantor | Naval Postgraduate School | |
| etd.thesisdegree.level | Masters | |
| etd.thesisdegree.name | Master of Science in Electrical Engineering | |
| relation.isDepartmentOfPublication | 88110183-ea50-46f5-b469-809c1418a16d | |
| relation.isDepartmentOfPublication.latestForDiscovery | 88110183-ea50-46f5-b469-809c1418a16d |
