Ambiguity Resolution in Direction of Arrival Estimation using Mixed Integer Optimization and Deep Learning
| Autor: | C. G. Davis, Joel Goodman, Daniel Salmond, Crystal Bertoncini Acosta |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Ambiguity resolution
Calibration (statistics) business.industry Deep learning Monte Carlo method Direction of arrival 020206 networking & telecommunications 02 engineering and technology Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering Artificial intelligence Imperfect business Algorithm Integer (computer science) |
| Zdroj: | 2019 IEEE National Aerospace and Electronics Conference (NAECON). |
| DOI: | 10.1109/naecon46414.2019.9057944 |
| Popis: | In this paper we present two novel approaches to unambiguously estimate the direction of arrival (DOA) of an RF source by an array of receive antennas whose positions can take-on any arbitrary geometry. The first approach employs a simple constrained integer optimization, while the second approach employs deep learning. In both approaches the impact of imperfect array calibration on the performance of DOA estimation is quantified. We demonstrate in Monte Carlo simulations that both approaches are capable of achieving super-resolution performance under imperfect array calibration conditions. It was found that the constrained integer optimization outperforms deep learning when one has an accurate physics model of the receiver imperfections, however deep learning was more robust to significant calibration errors. |
| Databáze: | OpenAIRE |
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