Systolic Kalman Filtering Based On QR Decomposition

M. J. Chen; K. Yao

doi:10.1117/12.942011

21 January 1988 Systolic Kalman Filtering Based On QR Decomposition

M. J. Chen, K. Yao

Proceedings Volume 0826, Advanced Algorithms and Architectures for Signal Processing II; (1988) https://doi.org/10.1117/12.942011
Event: 31st Annual Technical Symposium on Optical and Optoelectronic Applied Sciences and Engineering, 1987, San Diego, CA, United States

Abstract

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.

Citation Download Citation

M. J. Chen and K. Yao "Systolic Kalman Filtering Based On QR Decomposition", Proc. SPIE 0826, Advanced Algorithms and Architectures for Signal Processing II, (21 January 1988); https://doi.org/10.1117/12.942011

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