DocumentCode :
914495
Title :
Parallel implementation of the extended square-root covariance filter for tracking applications
Author :
Lee, Edward K B ; Haykin, Simon
Author_Institution :
Motorola Inc., Fort Lauderdale, FL, USA
Volume :
4
Issue :
4
fYear :
1993
fDate :
4/1/1993 12:00:00 AM
Firstpage :
446
Lastpage :
457
Abstract :
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
Keywords :
Kalman filters; parallel algorithms; Kalman gain; computational requirements; decoupling technique; extended square-root covariance filter; parallelism; tracking; tracking Kalman filter; Concurrent computing; Covariance matrix; Equations; Measurement standards; Nonlinear filters; Parallel architectures; Parallel processing; Sparse matrices; Target tracking; Very large scale integration;
fLanguage :
English
Journal_Title :
Parallel and Distributed Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9219
Type :
jour
DOI :
10.1109/71.219759
Filename :
219759
Link To Document :
بازگشت