Sequential unfolding SVD for tensors with applications in array signal processing
IEEE Transactions on Signal Processing
Tensor-based spatial smoothing (TB-SS) using multiple snapshots
IEEE Transactions on Signal Processing
Results of mobile channel sounding measurements in the 4.9 GHz public safety band
RWS'10 Proceedings of the 2010 IEEE conference on Radio and wireless symposium
Tensor algebra and multidimensional harmonic retrieval in signal processing for MIMO radar
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
SIAM Journal on Matrix Analysis and Applications
A New Truncation Strategy for the Higher-Order Singular Value Decomposition
SIAM Journal on Scientific Computing
| Hi-index | 0.01 |
Multidimensional harmonic retrieval problems are encountered in a variety of signal processing applications including radar, sonar, communications, medical imaging, and the estimation of the parameters of the dominant multipath components from MIMO channel measurements. R-dimensional subspace-based methods, such as R-D Unitary ESPRIT, R-D RARE, or R-D MUSIC, are frequently used for this task. Since the measurement data is multidimensional, current approaches require stacking the dimensions into one highly structured matrix. However, in the conventional subspace estimation step, e.g., via an SVD of the latter matrix, this structure is not exploited. In this paper, we define a measurement tensor and estimate the signal subspace through a higher-order SVD. This allows us to exploit the structure inherent in the measurement data already in the first step of the algorithm which leads to better estimates of the signal subspace. We show how the concepts of forward-backward averaging and the mapping of centro-Hermitian matrices to real-valued matrices of the same size can be extended to tensors. As examples, we develop the R-D standard Tensor-ESPRIT and the R-D Unitary Tensor-ESPRIT algorithms. However, these new concepts can be applied to any multidimensional subspace-based parameter estimation scheme. Significant improvements of the resulting parameter estimation accuracy are achieved if there is at least one of the R dimensions, which possesses a number of sensors that is larger than the number of sources. This can already be observed in the two-dimensional case.