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Parallel implementation of the extended square-root covariance filter for tracking applications

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2 Author(s)
Lee, E.K.B. ; Motorola Inc., Fort Lauderdale, FL, USA ; Haykin, Simon

Parallel implementations of the extended square-root covariance filter (ESRCF) for tracking applications are developed. The decoupling technique and special properties used in the tracking Kalman filter (KF) are employed to reduce computational requirements and to increase parallelism. The application of the decoupling technique to the ESRCF results in the time and measurement updates of m decoupled (n/m)-dimensional matrices instead of one coupled n-dimensional matrix, where m denotes the tracking dimension and n denotes the number of state elements. The updates of m decoupled matrices are found to require approximately m fewer processing elements and clock cycles than the updates of one coupled matrix. The transformation of the Kalman gain which accounts for the decoupling is found to be straightforward to implement. The sparse nature of the measurement matrix and the sparse, band nature of the transition matrix are explored to simplify matrix multiplications

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:4 ,  Issue: 4 )

Date of Publication:

Apr 1993

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