Direction of Arrival (DOA) estimation with digital arrays under unknown Gaussian distributed element location perturbation has detrimental effects to the performance of traditional DOA estimation techniques. This work proposes an artificial intelligence (AI) approach as a solution to this problem. A Deep Convolutional Neural Network (DCNN) is proposed and experimentation into network parameters, classification networks, and how the DCNN is applied to the DOA problem are studied. It is shown that this AI based approach is successful in estimating the DOA with perturbed arrays where traditional approaches fail.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4556 |
| Date | 01 June 2024 |
| Creators | Shaham, Mathew |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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