Systolic approach to square root information Kalman filtering

Abstract
The systolic approach to Kalman filtering is described, which reduces an algorithm to its computational components which can then be mapped onto a parallel processing array. Square root information processing is considered, and two new algorithms are derived from a least-squares viewpoint and are shown to be equivalent to the Kalman filter. Candidate systolic array architectures are developed, which compare favourably with recently published results.

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