Graduation Year

2026

Document Type

Thesis

Degree

M.S.

Degree Name

Master of Science (M.S.)

Degree Granting Department

Electrical Engineering

Major Professor

Stephen Saddow, Ph.D.

Committee Member

Ismail Uysal, Ph.D.

Committee Member

Gokhan Mumcu, Ph.D.

Keywords

Antenna Arrays, Digital Beamforming, Direction-of-Arrival, Multiple SIgnal Classification (MUSIC), Passive Emitter Localization, Software-Defined Radio

Abstract

Passive emitter localization is an important capability for wireless sensing, interference detection, and spectrum awareness. Classical timing-based localization methods estimate emitter position from differences in signal arrival time across distributed sensor platforms, but their performance depends on precise receiver synchronization and favorable signal bandwidth characteristics. This thesis investigates subspace-decomposition-based spatial parameter estimation as an alternative approach in which directional information is extracted from coherent antenna-array measurements. This work develops the theoretical foundation and signal-processing framework for angle-of-arrival estimation. Various spatial parameter estimators are presented as supporting array-processing methods before focusing on a well-known subspace-decomposition-based approach called the Multiple SIgnal Classification algorithm. This algorithm exploits the orthogonality between the signal and noise subspaces of spatial covariance structure to provide super-resolution angle-of-arrival estimation for multiple uncorrelated sources.

The behavior of this algorithm is evaluated through simulation using an idealized model under dynamic source configurations. The results show that source resolvability depends on the spatial covariance data structure, which can degrade when the source configuration or covariance estimate no longer provides clear separation between the signal and noise eigenspaces. A physical prototype platform is then implemented to evaluate the processing framework using measured data. The measured results follow the qualitative trends predicted by simulation, while also showing more restrictive performance due to real-world effects that perturb the spatial covariance structure. This work demonstrates that subspace decomposition can be practically applied to coherent SDR-based spatial parameter estimation, while identifying the key modeling and calibration limitations that govern real-world spatial parameter estimation performance.

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