Cooperative Parameter Tracking On The Unit Sphere Using Distributed Adapt-Then-Combine Particle Filters And Parallel Transport

This paper introduces a new distributed Adapt-then-Combine (ATC) diffusion algorithm for cooperative tracking of an unknown state vector that evolves on the unit hypersphere. The adapt step is implemented for a general nonlinear observation model and a dynamic state model defined on the hypersphere using a marginal particle filter (PF). The combine step in turn uses parallel transport to build Gaussian parametric approximations on a common tangent space to the spherical manifold. Performance results are compared to those of competing linear diffusion Extended Kalman Filters and non-cooperative PFs.