Autoregressive parameter estimation from noisy data

A least-squares based method for noisy autoregressive signals has been developed recently, which needs to neither prefilter noisy data nor perform parameter extraction. In this brief, a more computationally efficient procedure for estimating the measurement noise variance is developed, and then an efficient implementation of the method is presented. It is shown that this better way of implementation can considerably reduce the computational requirement of the least-squares based method without any performance degradation. Computer simulations that support the theoretical findings are given.