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The actual manifold vector (or steering vector) of the signal of interest (SOI) is often imprecise in practical applications. However the true manifold vector can be expressed as a product of a known matrix and an unknown coordinate vector in many cases. This model can accommodate many manifold uncertainties, for instance, the look direction error, local scattering, etc. Matched direction beamformer...
The traditional signal-subspace projection (SSP) method combats the problem of array manifold uncertainty to gain the robustness by means of projecting the nominal manifold vector onto the signal subspace so as to eliminate the errors lying in the noise subspace. The main contribution of this paper is to extent the SSP approach from one dimension to multi-dimension. We assume that the actual manifold...
In this paper, the manifold vector of the desired signal is assumed to lie in a known subspace, but the associated coordinates are random variables. This model is suitable for the fast varying environment. The so-called matched subspace beamforming is referred to as the beamformer resolving the signal that are drawn from this multi-dimension subspace. By jointly using such subspace and the signal...
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