  This paper develops a distributed collaborative localization algorithm based
on an extended kalman filter. This algorithm incorporates Ultra-Wideband (UWB)
measurements for vehicle to vehicle ranging, and shows improvements in
localization accuracy where GPS typically falls short. The algorithm was first
tested in a newly created open-source simulation environment that emulates
various numbers of vehicles and sensors while simultaneously testing multiple
localization algorithms. Predicted error distributions for various algorithms
are quickly producible using the Monte-Carlo method and optimization techniques
within MatLab. The simulation results were validated experimentally in an
outdoor, urban environment. Improvements of localization accuracy over a
typical extended kalman filter ranged from 2.9% to 9.3% over 180 meter test
runs. When GPS was denied, these improvements increased up to 83.3% over a
standard kalman filter. In both simulation and experimentally, the DCL
algorithm was shown to be a good approximation of a full state filter, while
reducing required communication between vehicles. These results are promising
in showing the efficacy of adding UWB ranging sensors to cars for collaborative
and landmark localization, especially in GPS-denied environments. In the
future, additional moving vehicles with additional tags will be tested in other
challenging GPS denied environments.
