Pose estimation of neighboring robots is a key requirement for configuration control behaviors in multi-robot systems. Estimating pose is difficult without system constraints, it is even more challenging when using minimalistic sensing alongside limited bandwidth. Minimal sensing models are a well studied field in robotics and are relevant to our particular hardware platform, the r-one, which has sensors that only measure a low-resolution bearing to neighboring robots. These bearing-only sensors are simpler to design with and cheaper to deploy in large numbers. In this thesis, I focus on the r-one multi-robot system which is capable of coarsely measuring the bearing, but not the distance, to neighbors. These sensors have a angular resolution of only 22.5 degrees due to the construction of the infrared system. I develop a particle filter algorithm that allows the r-one robot to estimate the pose of a neighbor using the infrared communication system and odometry measurements. This algorithm relies on the fusion of a coarse bearing measurement and neighbor velocities and is optimized to use the smallest communications bandwidth possible. I tested this algorithm with a simulation to demonstrate its effectiveness across varying sensor setups, neighbor update periods, and number of particles.