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 maximumlikelihood (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.