  We consider the line spectral estimation problem which aims to recover a
mixture of complex sinusoids from a small number of randomly observed time
domain samples. Compressed sensing methods formulates line spectral estimation
as a sparse signal recovery problem by discretizing the continuous frequency
parameter space into a finite set of grid points. Discretization, however,
inevitably incurs errors and leads to deteriorated estimation performance. In
this paper, we propose a new method which leverages recent advances in tensor
decomposition. Specifically, we organize the observed data into a structured
tensor and cast line spectral estimation as a CANDECOMP/PARAFAC (CP)
decomposition problem with missing entries. The uniqueness of the CP
decomposition allows the frequency components to be super-resolved with
infinite precision. Simulation results show that the proposed method provides a
competitive estimate accuracy compared with existing state-of-the-art
algorithms.
