Abstract
Navigation systems of autonomous vehicles often exploit range measurement information that may be affected by outliers. In marine application the presence of outliers in sonar bathymetry, for instance, can be particularly sever due to multipath phenomena in the acoustic propagation. The paper describes a possible approach to process range measurements highly contaminated by outliers. The proposed solution builds on a robust parameter identification algorithm minimizing a nonlinear cost function exploiting the mathematical properties of Gibbs entropy. Numerical examples on simulated data are provided to illustrate the method and its performance. The use of simulated data allows to vary the amount of noise and outlier contamination while knowing the ground truth values of the parameters to be identified.
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