Abstract:
A highly accurate DFT-based complex
exponential phase estimation algorithm is presented in this
paper. DFT-based signal parameter estimation algorithms
have the advantage of being computationally efficient and
are well suited for real time system implementation. It is
shown that DFT-based phase estimator is a maximum
likelihood (ML) estimator. This algorithm is an extension of
a recently published DFT-based frequency estimation
algorithm in [I] which is an evolution of the estimation
algorithm presented in [2]. It is shown that for large number
of samples N and large signal to noise ratio (SNR), the
phase estimation variance is 0.0475 dB above the Cramer-
Rao Bound (CRB). Exact phase estimation can be achieved
in the noiseless case with this algorithm. The algorithm has
low implementation computational complexity and is
suitable for numerous real time digital signal processing
applications.
