SQUARE ROOT PARALLEL KALMAN FILTERING USING REDUCED-ORDER LOCAL FILTERS

We examine various discrete-time parallel Kalman filtering implementations, with special attention given to square root versions in both covariance and information filter forms. Throughout the paper we employ the common convention of using the term "square root" to refer to a Cholesky factor. A special feature of the suggested architecture is the ability to accommodate parallel local filters that have a smaller state dimension than the global filter. The estimates and covariance or information matrices (or their square roots) from these reduced-order filters are collated at a central filter at each step to generate the full-size, globally optimal estimates and their associated error covariance or information matrices (or their square roots).