  In this paper, we consider the spectrum sensing in cognitive radio networks
when the impulsive noise appears. We propose a class of blind and robust
detectors using M-estimators in eigenvalue based spectrum sensing method. The
conventional eigenvalue based method uses statistics derived from the
eigenvalues of sample covariance matrix(SCM) as testing statistics, which are
inefficient and unstable in the impulsive noise environment. Instead of SCM, we
can use M-estimators, which have good performance under both impulsive and
non-impulsive noise. Among those M-estimators, We recommend the Tyler's
M-estimator instead, which requires no knowledge of noise distribution and have
the same probability of false alarm under different complex elliptically
symmetric distributions. In addition, it performs better than the detector
using sample covariance matrix when the noise is highly impulsive. It should be
emphasized that this detector does not require knowledge of noise power which
is required by the energy detection based methods. Simulations show that it
performs better than conventional detector using sample covariance matrix in a
highly impulsive noise environment.
