Adaptive spatial spectrum estimation via conjugate gradients and dominant mode rejection beamforming

Under the condition of low sample support, there is deviation between the estimated and true correlation matrices. In this case, the full-rank sample matrix inversion (SMI) beamformer is no longer optimal. Instead, a low-rank adaptive beamformer could yield better performance. When extending from a couple of signal directions to the entire spatial spectrum, the output power is a more applicable touchstone than SINR, which is only defined for signal directions. Adaptive algorithms (CG/DMR), if operating at the best rank for each direction, could effectively reveal the spatial spectrum. The best rank is obtained by investigating an ideal evaluation from the true correlation matrix and adaptive beamformer.