Systolic Kalman Filtering Based On QR Decomposition
M. J. Chen, Kaiyuan Yao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1988
In this paper, by using the matrix decomposition method, the Kalman filter can be formulated as a modified SRIF data processing problem followed by a QR operation. Compared with the conventional SRIF method, this approach simplifies the computational structure, and is more reliable when the system has a singular(or near singular) coefficient matrix. By skewing the order of input matrices, fully pipelined systolic2Kalman filtering operation can be achieved. With the number of processing units of the 0(n ), the system throughput rate is of the 0(n). The numerical properties of the systolic Kalman filtering algorithm under finite word length effect are studied via analysis and computer simulations, and are compared with those of conventional approaches.