High-precision frequency estimation of real sinusoids with reduced computational complexity using a model-based matched-spectrum approach

Frequency-estimation algorithms devised for complex sinusoids, including the maximum-likelihood (ML) approach, when operating on real sinusoidal signals, suffer from spectral interference due to the superposition of the aliasing components at negative and positive frequencies. This paper introduces a frequency estimation ML-like algorithm, based on a spectral-matching approach, that avoids such superposition effect by incorporating it in the signal/spectrum model. As a result, the proposed method is able to generate a more precise frequency estimate in comparison to previous approaches at a comparable computational cost, as endorsed by provided computational analyses and simulation results.