Teams of collaborative robots or rovers are expected to play a major role in future aerospace applications, including exploration of the lunar surface. Efficient, reliable methods to determine the position, velocity, and timing status of the rovers are required to facilitate this work and reduce risk of interference. This thesis presents two distributed Kalman Filter-based estimation algorithms for fully-connected networks using time-of-flight measurements. The network is composed of static beacons at known locations and mobile rovers whose positions and velocities are to be determined. Beacons and rovers are collectively called agents. Each agent’s filter tracks the position, velocity, and time (PVT) states of all of the rovers in addition to the time states of the beacons. Knowledge of all position and velocity states enables rovers to accomplish collaborative tasks without interfering with each other; tracking time states supports the use of time-of-flight relative range measurements. This work addresses the limitations in the state of the art by introducing two decentralized algorithms where each agent can estimate the time-varying position, velocity, and clock bias/bias rate of itself and all other agents in a distributed fashion. In the first method, agents exchange full PVT state estimates, which are conservatively fused with local estimates using covariance intersection. In many applications it is desirable to reduce the amount of data transmission required for navigation purposes to free up space for other purposes. To that end, the second method reduces communication packet size by only sending sufficiently surprising measurements as determined by an event-triggering threshold. Simulation results using a low-quality clock with measurements precise on the order of centimeters show that the covariance intersection method achieves a 2D position RMSE error of 0.5 m. The event-triggering method significantly reduces required communication, but results in larger errors of about 1.8 m for a moderate triggering threshold of δ = 2 m.</p
