NASA is planning future missions involving fleets of small satellites in LEO and GEO that can exhibit autonomous collective behavior. Such a "swarm of femto-satellites" can complete complex tasks that are difficult or impossible for large single satellites to achieve. Swarm state estimation can be achieved using the available GPS measurements, and local range and bearing measurements between the satellites. Due to the large size of the swarms and processing constraints on-board each femto-satellite, distributed estimation algorithms are necessary for successful implementation of estimation algorithms. However, measurements of range and bearing are nonlinear and couple the states of neighboring satellites, hence requiring nonlinear filtering algorithms that complicate the process of decentralizing the estimator. The objective of this project is to determine efficient estimator architectures and algorithms for distributed state estimation of femtosat swarms in LEO. SSCI has designed estimators and performed trade studies for unbounded swarms in deep-space as part of a DARPA study at NASA JPL. The proposed Phase-I project extends our previous works to state estimation for bounded swarms in LEO where swarm dynamics are nonlinear.