New results on reduced rank and polynomial order method

A subspace-based reduced rank and polynomial order (RRPO) method was proposed recently, which estimates an r/sup th/ order linear prediction polynomial whose roots are the desired "signal roots". In this paper, we give some new results on the RRPO method, which include projection based solutions, low rank signal-only correlation matrix based solutions, model overfitting solutions, and noise subspace transformation based solutions. These various approaches give us more freedom to design different algorithms for different conditions. Simulation results indicate that model overfitting outperforms previously proposed RRPO methods.