Parallel and sequential block Kalman filtering and their implementations using systolic arrays Azimi-Sadjadi, Mahmood R. ; Lu, Tongxin ; Nebot, Eduardo Mario Two sets of block Kalman filtering equations are derived that differ in the manner of generating the initial and updated estimates. Parallel and sequential schemes for generating these estimates are adopted. It is shown that the parallel implementation inherently leads to a block Kalman estimator which provides filtered estimates at the vector (block) level and fixed-lag smoothed estimates at the sample level. The sequential implementation scheme, on the other hand, generates the estimates of each sample recursively, leading naturally to a scalar (filter) estimator. These scalar estimates are arranged in a vector form, resulting in a block estimator which solely generates filtered estimates both at the vector and sample levels. Simulation results on a speech signal are also presented which indicate the advantages of the sequential block Kalman filter. An algorithm for iterative calculation of Kalman gain and error covariance matrices is given which does not require any matrix inversion operation. The implementation of this algorithm using available systolic array processors is presented. A ring systolic array is also suggested which can be used to implement the state update part of the block Kalman filter. Colorado State University. Libraries 1991 text ; image application/pdf ECEmra00031.pdf FACFECEN100511ARTI eng c1991 IEEE
Parallel and sequential block Kalman filtering and their implementations using systolic arrays
Azimi-Sadjadi, Mahmood R. ; Lu, Tongxin ; Nebot, Eduardo Mario
Two sets of block Kalman filtering equations are derived that differ in the manner of generating the initial and updated estimates. Parallel and sequential schemes for generating these estimates are adopted. It is shown that the parallel implementation inherently leads to a block Kalman estimator which provides filtered estimates at the vector (block) level and fixed-lag smoothed estimates at the sample level. The sequential implementation scheme, on the other hand, generates the estimates of each sample recursively, leading naturally to a scalar (filter) estimator. These scalar estimates are arranged in a vector form, resulting in a block estimator which solely generates filtered estimates both at the vector and sample levels. Simulation results on a speech signal are also presented which indicate the advantages of the sequential block Kalman filter. An algorithm for iterative calculation of Kalman gain and error covariance matrices is given which does not require any matrix inversion operation. The implementation of this algorithm using available systolic array processors is presented. A ring systolic array is also suggested which can be used to implement the state update part of the block Kalman filter.
Colorado State University. Libraries
1991
text ; image
application/pdf
ECEmra00031.pdf
FACFECEN100511ARTI
eng
c1991 IEEE