Abstract:
The determination of the number of signals in a wide class of problems, including array processing, harmonic retrieval and pole retrieval, is addressed. A new approach, b...Show MoreMetadata
Abstract:
The determination of the number of signals in a wide class of problems, including array processing, harmonic retrieval and pole retrieval, is addressed. A new approach, based on the application of the information theoretic criteria for model identification introduced by Akaike, Schwartz and Rissanen, is presented. It is shown that the criterion introduced by Schwartz and Rissanen yields a consistent estimate of the number of signals, while the criterion introduced by Akaike yields an inconsistent estimate that tends, in the large-sample limit, to overestimate the number of signals.
Date of Conference: 19-21 March 1984
Date Added to IEEE Xplore: 29 January 2003
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Information Theory ,
- Identification Criteria ,
- Array Processing ,
- Maximum Likelihood ,
- Degrees Of Freedom ,
- Eigenvalues ,
- Signal Processing ,
- Covariance Matrix ,
- Maximum Likelihood Estimation ,
- Log-likelihood ,
- Eigenvectors ,
- Additive Noise ,
- Natural Response ,
- Vector Matrix ,
- Observation Vector ,
- Sample Covariance Matrix ,
- Minimum Description Length ,
- Curly Brackets ,
- Covariance Matrix Of Vector
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Information Theory ,
- Identification Criteria ,
- Array Processing ,
- Maximum Likelihood ,
- Degrees Of Freedom ,
- Eigenvalues ,
- Signal Processing ,
- Covariance Matrix ,
- Maximum Likelihood Estimation ,
- Log-likelihood ,
- Eigenvectors ,
- Additive Noise ,
- Natural Response ,
- Vector Matrix ,
- Observation Vector ,
- Sample Covariance Matrix ,
- Minimum Description Length ,
- Curly Brackets ,
- Covariance Matrix Of Vector