Algorithms for Accurate Spectral Analysis in the Presence of Arbitrary Noncoherency and Large Distortion

In spectral analysis, achieving coherent sampling, especially when signals have large distortion, has been a challenge for many years. This paper introduces three algorithms to resolve this issue. In comparison to previous algorithms, and two widely used methods in industry: windowing and four-parameter sine wave fit, these new algorithms are capable of obtaining accurate spectral results of the signal, while achieving high accuracy as well as computational efficiency. The novel contribution of this paper is that not only does it propose two new algorithms, but also it analyzes their advantages and limitations in detail, providing their trade-offs and different fields of applications. Extensive simulations and measurements are performed to validate these algorithms. Combined with their high accuracy, computational efficiency, and robustness against signal purity, these algorithms are readily available to be implemented for bench or on-chip testing. In addition, it is suitable for data converter spectral testing when noncoherent sampling is present, and spectrally pure test signal source is not available.