Exact Decomposition of Multifrequency Discrete Real and Complex Signals

TL;DR

This paper introduces two new theorems enabling exact decomposition of multifrequency signals, eliminating spectral leakage and the picket fence effect, with high resolution even in noisy conditions and without strict Nyquist sampling.

Contribution

The paper presents novel decomposition theorems that completely eliminate spectral leakage and the picket fence effect, enabling exact signal component extraction from fewer samples.

Findings

Exact decomposition of real signals using 4m-1 samples

Exact decomposition of complex signals using 2m-1 samples

High resolution and noise robustness demonstrated

Abstract

'The spectral leakage from windowing and the picket fence effect from discretization' have been among the standard contents in textbooks for many decades. The spectral leakage and picket fence effect would cause the distortions in amplitude, frequency, and phase of signals, which have always been of concern, and attempts have been made to solve them. This paper proposes two novel decomposition theorems that can totally eliminate the spectral leakage and picket fence effect, and could broaden the knowledge of signal processing. First, two generalized eigenvalue equations are constructed for multifrequency discrete real signals and complex signals. The two decomposition theorems are then proved. On these bases, exact decomposition methods for real and complex signals are proposed. For a noise-free multifrequency real signal with m sinusoidal components, the frequency, amplitude, and phase of each component can be exactly calculated by using just 4m-1 discrete values and its second-order derivatives. For a multifrequency complex signal, only 2m-1 discrete values and its first-order derivatives are needed. The numerical experiments show that the proposed methods have very high resolution, and the sampling rate does not necessarily obey the Nyquist sampling theorem. With noisy signals, the proposed methods have extraordinary accuracy.