Low-Complexity High-Resolution Frequency Estimation of Multi-Sinusoidal Signals

High-resolution frequency estimation is crucial for some applications. Accordingly, this article proposes three high-performance computationally efficient (HPCE) methods for high-resolution frequency estimators, which are designed based on a modified likelihood function. Traditional maximum likelihood-based approaches for high-resolution frequency estimation are inefficient since the associated optimization problem is nonconvex. Accordingly, in the first estimator proposed in this study, the amplitudes and frequencies of the multi-sinusoidal signals are estimated iteratively based on a simple linear Taylor approximation and a low-dimensional closed-form solution in every iteration. In the second estimator, the frequencies are determined directly using a primal decomposition approach and a gradient descent search method. Finally, a novel low-complexity parallel interference cancellation (PIC)-based frequency estimation approach is developed. The simulation results show that the proposed designs not only meet the Cramér–Rao lower bound (CRLB) in most cases of the conducted examples but also possess lower computational complexity than existing state-of-the-art approaches.