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Learning the Barankin Lower Bound on DOA Estimation Error

DOI:
10.60864/chzs-b118
Citation Author(s):
Submitted by:
Hai Habi
Last updated:
6 June 2024 - 10:50am
Document Type:
Presentation Slides
Document Year:
2024
Event:
Presenters:
Hai Victor Habi
Paper Code:
SPTM-P8.4
 

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.

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