Abstract
The Kalman filter is essentially a recursive data processing algorithm which provides an optimal estimate of the state vector of a system from noisy measurements. A drawback of the algorithm is that it is computationally intensive, requiring powerful processing systems to meet the sample time requirements of real-time systems. This has led to the development of parallel versions of the Kalman algorithm, in order to reduce execution times and enable the algorithm to be implemented in an increased range of real-time applications. In this work, a number of parallel Kalman algorithms are mapped onto a number of different parallel processing systems, including SIMD and MIMD architectures. The different parallel approaches are then compared for speed of execution and efficient use of processing power.
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