Abstract
We introduce the Generative Barankin Bound (GBB), a learned Barankin Bound, for evaluating the achievable performance in estimating the direction of arrival (DOA) of a source in non-asymptotic conditions, when the statistics of the measurement are unknown. We first learn the measurement distribution using a conditional normalizing flow (CNF) and then use it to derive the GBB. We show that the resulting learned bound approximates the analytical Barankin bound well for the case of a Gaussian signal in Gaussian noise, Then, we evaluate the GBB for cases where analytical expressions for the Barankin Bound cannot be derived. In particular, we study the effect of non-Gaussian scenarios on the threshold SNR.
| Original language | English |
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| Title of host publication | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 9906-9910 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798350344851 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
| Event | 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of Duration: 14 Apr 2024 → 19 Apr 2024 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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| ISSN (Print) | 1520-6149 |
Conference
| Conference | 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 |
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| Country/Territory | Korea, Republic of |
| City | Seoul |
| Period | 14/04/24 → 19/04/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- beam-pattern
- DOA estimation
- Generative Models
- Normalizing Flow
- Performance Bound