Higher Order SVD Based Subspace Estimation to Improve Multi-Dimensional Parameter Estimation Algorithms

MIMO channel modeling from channel sounder measurements requires the use of high-resolution parameter estimation algorithms. Multi-dimensional subspace-based methods, such as R-D Unitary ESPRIT, are frequently used for this task. Since the measurement data is multi-dimensional, current approaches require stacking the dimensions into one highly structured matrix. In the conventional subspace estimation step, e.g., via an SVD of this highly structured 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 mapping onto the real-valued domain can be extended to tensors. As an example, we discuss the impact on the accuracy of the R-D Unitary ESPRIT algorithm. However, these new concepts can be applied to any multi-dimensional subspace-based parameter estimation scheme.