Low-complexity architecture for AR(1) inference

In this Letter, the authors propose a low-complexity estimator for the correlation coefficient based on the signed $\operatorname {\rm AR}\lpar 1\rpar $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 the literature with maximum error in order of $10^{-2}$10−2. However, the hardware implementation of the introduced method presents considerable advantages in several relevant metrics, offering more than 95% reduction in dynamic power and doubling the maximum operating frequency when compared to the reference method.