This paper focuses on a detailed analysis of the ESPRIT algorithm, claiming optimal error scaling under specific assumptions, particularly when noise is minimal. A key contribution is theorem-based proof utilizing the concept of total variation distance between Gaussian distributions. The authors also establish connections to standard bounds in signal processing, highlighting flexibility in proof application even under different settings. Additionally, the paper includes various appendix sections outlining relevant preliminary information and deferred proofs to support the methodologies and findings presented.
The main contribution of this paper is a novel analysis of the ESPRIT algorithm, demonstrating optimal error scaling, notably under the premise of small noise conditions.
This paper offers a self-contained proof of the optimal error scaling for the ESPRIT algorithm by employing total variation distance, drawing parallels to the Cramér-Rao bound.
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