Abstract
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.
Original language | English (US) |
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Pages (from-to) | 956-965 |
Number of pages | 10 |
Journal | IEEE Transactions on Signal Processing |
Volume | 50 |
Issue number | 4 |
DOIs | |
State | Published - Apr 2002 |
Keywords
- Inverse scattering
- Model order selection
- Multipath channels
- Nested nonlinear models
- Overparametrized models
- Stochastic complexity
- Superimposed models
- Wald statistic
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering