Linear constrained reduced rank and polynomial order methods

The Subspace-based Reduced Rank and Polynomial Order (RRPO) methods were proposed recently [1, 2, 3], which estimate a reduced order linear prediction polynomial whose roots are the desired "signal roots". In this paper, we describe how to extend the RRPO methods to include constraints involving known signal information. Simulation results indicate that by incorporating known signal information such as source direction angle, the estimation of unknown source directions can be signi cantly improved, especially when the unknown source is weak, closely spaced and highly coherent with the known source.