Wald statistic for model order selection in superposition models

A consistent model selection algorithm is presented for superimposed signal models. The proposed method is motivated by the Wald statistic and reduces the computational complexity of procedures based on the minimum description length (MDL) principle. The procedure is suggested when a noncyclostationary signal model or short data length prevents use of covariance rank test. For maximum model-order K, the procedure provides O(K) computational savings over an MDL test. Additionally, a proof establishes the consistency of a least-squares estimator using overparametrized. models. Finite sample performance of the proposed model selection method is studied via Monte Carlo simulations for estimating the multipath delays and amplitudes of a chirp signal.