Performance Analysis of Information Theoretic Learning-Based Cooperative Localization

In the context of localization over ad-hoc wireless networks, the effect of non-line-of-sight (NLoS) presents a severe performance bottleneck that causes outages in the signal-to-noise ratio (SNR) of the returns and significantly increases the location estimates' error-floor. The non-Gaussianity and the scenario-dependent distributions/statistics of the additive noise-process caused by the NLoS returns make the naive ML-based solutions sub-optimal. In this regard, information-potential (IP) based localization algorithms have recently been found viable, especially in the presence of arbitrary additive noise-processes. This letter presents analytical insights on the error-bounds of two IP based algorithms in a cooperative-localization context, namely, maximum correntropy (MCC) and the minimum error entropy with fiducial points (MEE-FP). The presented analytical results for the considered IP based approaches are validated using relevant computer-simulations assuming typical point-processes.