High resolution nearly-ML estimation of sinusoids in noise using a fast frequency domain approach

Estimating the frequencies, amplitudes and phases of sinusoids in noise is a problem which arises in many-applications. The aim of the methods in this paper is to achieve computational efficiency and near-ML performance (i.e. low bias, variance and threshold SNR), in problems such as vibration or audio analysis where the number of tones may be large (e.g. > 20). An approach has recently been published for resolved tones [4]. This paper extends that frequency domain approach to the high-resolution problem.