Spatial Spectrum Estimation Theory and Algorithms
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Spatial Spectrum Estimation Theory and Algorithms - Signal Source Number Estimation using AIC, MDL, and HQ Algorithms with Implementation Approaches
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The paper discusses Spatial Spectrum Estimation Theory and Algorithms, which covers methods for estimating the number of signal sources, including implementations using Akaike Information Criterion (AIC), Minimum Description Length (MDL), and Hannan-Quinn (HQ) algorithms. These information-theoretic criteria are commonly implemented through eigenvalue decomposition of the sample covariance matrix, where the number of signals is determined by minimizing the criteria function over possible source counts. The algorithms typically involve calculating penalty terms that balance model fit against complexity, with AIC being asymptotically efficient, MDL consistent, and HQ providing a intermediate approach between the two.
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