  Spectrum sensing is essential in cognitive radio. By defining leading
\textit{eigenvector} as feature, we introduce a blind feature learning
algorithm (FLA) and a feature template matching (FTM) algorithm using learned
feature for spectrum sensing. We implement both algorithms on Lyrtech software
defined radio platform. Hardware experiment is performed to verify that feature
can be learned blindly. We compare FTM with a blind detector in hardware and
the results show that the detection performance for FTM is about 3 dB better.
