A subspace leakage suppression technique for high resolution processing of dispersive GPR signals

Abstract

Linear prediction methods, based on a Hankel data matrix, suffer from subspace leakage and degraded resolution when applied to data models that do not result in a mode matrix with Vandermonde structure, such as the constant-Q model. In the absence of noise, the Vandermonde structure ensures the equivalence between the number of backscattered signals and the rank of the data matrix. This paper first identifies the origin of subspace leakage residing in linear prediction methods when applied to data of the constant-Q model. Then it proposes a frequency-distortion technique, based on the extension theorems, for suppressing this leakage and preserving the time resolution performance of subspace-based and linear prediction data processing methods.