The impact of unknown extra parameters on scatter matrix estimation and detection performance in complex t-distributed data

Scatter matrix estimation and hypothesis testing in Complex Elliptically Symmetric (CES) distributions often relies on the knowledge of additional parameters characterizing the distribution at hand. In this paper, we investigate the performance of optimal estimation and detection algorithms exploiting low-complexity but suboptimal estimates of the extra parameters under the assumption of t-distributed data. Their performance is also compared with that of robust algorithms, which do not rely on such estimates.

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