  In this paper, we investigate super robust estimation approaches, which
generate a reliable estimation even when the noise observations are more than
half in an experiment. The following preliminary research results on super
robustness are presented: (1) It is proved that statistically, the maximum
likelihood location estimator of exponential power distribution (or L^p
location estimator, for short) is strict super robust, for a given p<1. (2) For
a given experiment and a super robust estimator family, there is an estimator
that generates an estimation that is close enough to a perfect estimation, for
general transformation groups. (3)L^p estimator family is a super robust
estimator family. (4) For a given experiment, L^p estimator on translation,
scaling and rotation generates perfect estimation when p is small enough, even
for very noisy experiments.
