Parallelization of the Kalman filter on multicore computational platforms

Parallelization of the Kalman filter algorithm, with emphasis on the specific demands of multicore architecture implementation, is investigated. The approach is based on the nonrestrictive assumption of a banded system matrix. Both time-varying and time-invariant systems can be generally transformed to such a form. The proposed method is applied to a radio interference power estimation problem for which speedup evaluations using up to eight cores are performed. It is shown that the algorithm is capable of achieving linear speedup in the number of cores used, while speedup factors for a parallel BLAS implementation are less than two. An algorithm analysis that provides guidelines to the choice of implementation hardware to meet a desired performance is also provided.

• A parallel implementation of the Kalman filter is suggested.
• Linear speedup in the number of cores used is achieved.
• Analysis of the amount of parallel work, communication and memory bandwidth is given.
• Exemplified by a radio inference power estimation problem.
• Comparison to a BLAS implementation is provided.