Recent work has developed a number of architectures and algorithms for accurately estimating spacecraft and formation states. The estimation accuracy achievable during spacecraft operation depends not only on the algorithm, but also on its implementation. Typically, the algorithm will be implemented on a real-time multi-tasking processor that allocates on-board computational resources to multiple tasks and functions according to some scheduling policy. The processor's task scheduler may induce delays that were unaccounted for at design-time and may sometimes preempt estimation tasks in favor of other tasks. Hence, estimation accuracy and in general the performance of any embedded algorithm can be significantly lower than expected during execution. The goal of this project is to develop distributed spacecraft state estimation algorithms that account for real-time multi-tasking processor and other implementation related resource constraints. We bring together modeling techniques from multi-class queuing, well-known Kalman filtering techniques and recent advances in embedded systems to develop an innovative co-design framework for the design of embedded state estimation algorithms and software. During the proposed effort, we will design, implement and evaluate estimation algorithms on a network of real-time processors or hardware emulations of processors on-board formation spacecraft.