We propose a novel robust estimation algorithm-the generalized projection-based M-
estimator (gpbM), which does not require the user to specify any scale parameters. The
algorithm is general and can handle heteroscedastic data with multiple linear constraints for
single and multicarrier problems. The gpbM has three distinct stages-scale estimation,
robust model estimation, and inlier/outlier dichotomy. In contrast, in its predecessor pbM,
each model hypotheses was associated with a different scale estimate. For data containing …

We introduce a robust estimator called generalized projection based M-estimator (gpbM)
which does not require the user to specify any scale parameters. For multiple inlier
structures, with different noise covariances, the estimator iteratively determines one inlier
structure at a time. Unlike pbM, where the scale of the inlier noise is estimated
simultaneously with the model parameters, gpbM has three distinct stages-scale estimation,
robust model estimation and inlier/outlier dichotomy. We evaluate our performance on …