In this paper we discuss robust cooperative local- ization in mixed line-of-sight and non-line-of-sight environments using time-of-arrival measurements. The mixed line-of-sight and non-line-of-sight environment is statistically modeled using a contaminated Gaussian mixture model in which the non-line- of-sight propagations are assumed to cause a positive bias in the time-of-arrival measurements. The non-line-of-sight propagations severely degrade the performance of localization algorithms which assume line-of-sight propagations. The maximum likeli- hood cooperative localization estimation is a highly non-linear and non-convex optimization problem which cannot be solved in a closed-form. Hence, we propose an approximate iterative robust cooperative localization algorithm to mitigate the impact of the non-line-of-sight propagations. The proposed robust localization algorithm yields a satisfactory performance in the presence of the non-line-of-sight propagations which would otherwise severely degrade the localization performance. Monte Carlo simulations show that the proposed robust cooperative localization algorithm is indeed robust to the increase in the ratio of the number of the non-line-of-sight propagations to line-of-sight propagations and the strength of the non-line-of-sight propagations.
['Behailu Y. Shikur', 'Tobias Weber']
Robust cooperative localization in mixed LOS and NLOS environments using TOA