Robust Multiple Signal Classification via Probability Measure Transformation

DOI10.1109/TSP.2014.2388436zbMath1394.94600arXiv1508.01625OpenAlexW2030255407MaRDI QIDQ4579767

Publication date: 22 August 2018

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Abstract: In this paper, we introduce a new framework for robust multiple signal classification (MUSIC). The proposed framework, called robust measure-transformed (MT) MUSIC, is based on applying a transform to the probability distribution of the received signals, i.e., transformation of the probability measure defined on the observation space. In robust MT-MUSIC, the sample covariance is replaced by the empirical MT-covariance. By judicious choice of the transform we show that: 1) the resulting empirical MT-covariance is B-robust, with bounded influence function that takes negligible values for large norm outliers, and 2) under the assumption of spherically contoured noise distribution, the noise subspace can be determined from the eigendecomposition of the MT-covariance. Furthermore, we derive a new robust measure-transformed minimum description length (MDL) criterion for estimating the number of signals, and extend the MT-MUSIC framework to the case of coherent signals. The proposed approach is illustrated in simulation examples that show its advantages as compared to other robust MUSIC and MDL generalizations.

Full work available at URL: https://arxiv.org/abs/1508.01625

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)