Dynamic Clustering for GNSS Positioning with Multiple Receivers

We consider the problem of jointly estimating the states of multiple global navigation satellite system (GNSS) receivers modeled with shared biases. In particular, we explore how to best assign these receivers to disjoint sets, so as to retain computational feasibility in the resulting filters. We propose a genetic algorithm that dynamically assigns agents to clusters subject to constraints on the maximum number of states in the clusters. Several numerical examples illustrate the flexibility of the approach, and the choice of genetic operations in the clustering algorithm is motivated by their effect on the algorithm's expected convergence rate. Numerical experiments with a GNSS-inspired problem demonstrates that the proposed clustering can yield a substantial improvements in the mean-square error compared to a random cluster assignment.