Multi-dimensional harmonic estimation using K-D RARE in application to MIMO channel estimation

In this paper, a new approach to the multi-dimensional harmonic retrieval problem is proposed. The novel method is based on a multi-dimensional extension of the Rank Reduction Estimator (RARE), originally developed for DOA estimation in partly calibrated arrays. In the K-D RARE algorithm the frequency parameters in the various dimensions are sequentially estimated. The dimensionality of the estimation problem and therefore the computational load of the optimization procedure is successively reduced exploiting the rich multidimensional structure of the estimation problem. This important property yields various benefits like high estimation performance, weak identifiability conditions and automatically associated parameter estimates. The performance of the algorithm is illustrated with the example of MIMO communication channel estimation based on the double-directional channel model. Numerical examples based on simulated and measured data recorded from the RUSK vector channel sounder at 2 GHz are presented.