a variety of applications such as target tracking using radar or lidar, and localisation using radio waves such as is done in for instance global satellite navigation systems [@article:ITVT_kok_15; @article:ITVT_chen_09; @article:ISPM_gustafsson_05; @article:IME_eling_12]. For example, the error histograms of time-of-arrival measurements collected from three separate cellular antennas are given in Figure \[fig:kista\]. For detailed description of hardware and the measurement campaign see [@conf:PIMRC_medbo_09]

To deal with the estimation’s performance degradation in non-Gaussian error conditions, conventional estimation techniques which are developed based on Gaussian assumptions need to be adjusted properly. As discussed in [@article:ITSP_yin_13], “identify and discard”, “mathematical programming”, and “robust estimation” are the three broad categories of estimation methods which are robust against non-Gaussian errors. Robustness of the estimator has been a concern for many years in both research [@article:JASA_stigler_73] and different engineering topics  [@article:PI_kassam_85; @book:SDNGN_kassam; @article:SIAM_stewart_99; @book:NSP_arce] for a long time now. A more recent survey on this topic containing more references can be found in [@article:ISPM_zoubir_12].

The maximum likelihood estimator (MLE), developed under Gaussian assumptions, can be modified to become robust in presence of non-Gaussian noises. The authors in [@conf:ICML_eskin_00] first detect and then reject the outliers by learning the probability density function (PDF) of the measurements and develop 