Adaptive ML signal detection in non-Gaussian channels

The problem of robust signal detection in non-Gaussian noise is revisited. In this paper, we look at some issues of robust estimators which have been discussed very little in previous works. Some robust estimators, which are adaptive in nature and asymptotically efficient, are introduced and some technical improvements are suggested. Performance of these robust estimators is given in a practical communication problem and their asymptotic properties are investigated when the parameter-to-observation ratio becomes large.