The authors address the problem of estimating the number of components in a multibaseline interferometric synthetic aperture radar (InSAR) signal, corrupted by complex correlated multiplicative noise, in the presence of the layover phenomenon. The appearance of multiplicative noise, termed 'speckle', makes this problem very atypical. In fact, all the approaches proposed in literature have been applied to constant amplitude sinusoidal signals. In particular, the information theoretic criteria (ITC) have been conceived for estimating the number of signal components embedded in additive white noise. In this case, the problem is equivalent to the estimation of the multiplicity of the smallest eigenvalues of the data covariance matrix. In the presence of multiplicative noise, the signal eigenvalues spectrum changes. Consequently, the classic ITC methods operate under model mismatch. In a previous work, the authors investigated their robustness to speckle. An ad hoc algorithm for model order selection, based on Capon and least squares methods is developed. Its performance is analysed via Monte Carlo simulation and compared with those of classic ITC.
Capon-LS for Model Order Selection of Multicomponent Interferometric SAR Signals
GINI, FULVIO;VERRAZZANI, LUCIO
2004-01-01
Abstract
The authors address the problem of estimating the number of components in a multibaseline interferometric synthetic aperture radar (InSAR) signal, corrupted by complex correlated multiplicative noise, in the presence of the layover phenomenon. The appearance of multiplicative noise, termed 'speckle', makes this problem very atypical. In fact, all the approaches proposed in literature have been applied to constant amplitude sinusoidal signals. In particular, the information theoretic criteria (ITC) have been conceived for estimating the number of signal components embedded in additive white noise. In this case, the problem is equivalent to the estimation of the multiplicity of the smallest eigenvalues of the data covariance matrix. In the presence of multiplicative noise, the signal eigenvalues spectrum changes. Consequently, the classic ITC methods operate under model mismatch. In a previous work, the authors investigated their robustness to speckle. An ad hoc algorithm for model order selection, based on Capon and least squares methods is developed. Its performance is analysed via Monte Carlo simulation and compared with those of classic ITC.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
