HOSVD-Based Subspace Algorithm for Multidimensional Frequency Estimation Without Pairing Parameters

In this paper, a new method for multidimensional frequency estimation of multiple sinusoids that combines the HOSVD (Higher-order singular value decomposition) subspace and projection separation approaches is presented. Frequency parameters in the first dimension are obtained by using the signal subspace of the first dimension which is extracted by the HOSVD decomposition. Subsequently, a set of projection separation matrices is constructed to project the measure tensor and separate the components of the received tensor into single ones. And then, the signal subspace of each dimension of separated measure tensor are estimated by the HOSVD decomposition and the desired multidimensional frequency pairing are automatically obtained. Simulation results are included to demonstrate the advantage of the proposed method over two existing methods in terms of performance as well computational load.