The dual channel sinewave model: Co-prime sparse sampling, parameter estimation, and the Cramér–Rao Bound

The seven-parameter sine fit algorithm for a dual channel signal model with common frequency has been considered in a prior work as an accurate procedure for parameter estimation. In this paper, we investigate an approach in the dual channel sine fit where the signals from both channels are uniformly subsampled with coprime pair of sparse samplers and present analysis of the frequency estimation problem from undersampled measurements thereby relaxing the demand on devices and instruments by lowering the required sampling frequency as well as bandwidth. The Cramér–Rao Bound based on the coprime subsampled data set is derived and numerical illustrations are given showing the relation between the cost in performance based on the mean squared error and the employed coprime factors for a given measurement time.

► We presented an estimation technique employing coprime subsampled dual channel model.
► We derive CRB for the coprime subsampled dual channel model.
► We showed the tradeoff between performance and reduction in sampling frequency.