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In many signal processing algorithms the estimation of signal co-variance matrices is a key task. In many applications using tensor representation for the signals provides significant benefits in deriving new algorithms and revealing interesting signal properties. It is natural to model many signals in MIMO communications, physics, principal component analysis, or medical imaging using tensors. It...
The paper presents novel, computationally efficient methods for tracking radio propagation path parameters from measured tensor-valued MIMO observations. The measurement covariance model is comprised of spatio-temporal scattering plus measurement noise, leading to a shifted Kronecker structure. We derive novel expressions to handle these large (105 times 105) covariance matrices in the extended Kalman...
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