Low-complexity Architecture for AR(1) Inference

08/21/2020

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by   A. Borges Jr., et al.

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In this Letter, we propose a low-complexity estimator for the correlation coefficient based on the signed AR(1) process. The introduced approximation is suitable for implementation in low-power hardware architectures. Monte Carlo simulations reveal that the proposed estimator performs comparably to the competing methods in literature with maximum error in order of 10^-2. However, the hardware implementation of the introduced method presents considerable advantages in several relevant metrics, offering more than 95 frequency when compared to the reference method.