Parallel structures for Kalman filtering

In this paper we examine various discrete-time parallel Kalman filtering implementations, with special attention given to square-root versions in both covariance and information filter forms. A special feature of the suggested architecture is the ability to accomodate parallel local filters that have a smaller state dimension than the global filter. The estimates and covariances from these reduced-order filters are collated at a central filter at each step to generate the full-order, globally optimal estimates and their associated error covariances.