Non-linear smoothing algorithm for multi-dimensional dynamic systems |
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Authors: | KERİM DEMİRBAŞ |
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Affiliation: | University of Illinois at Chicago, Department of Electrical Engineering and Computer Science (M/C 154) , P.O. Box 4348, Chicago, Illinois, 60680, U.S.A. |
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Abstract: | A new sub-optimum smoothing algorithm is presented for multi-dimensional dynamic systems. This algorithm is based upon quantization, multiple hypothesis testing, and the Viterbi decoding algorithm. The estimation of state vectors is carried out sequentially, component-by-component, and in parallel. A considerable memory reduction is achieved for state estimation implementation with the proposed algorithm. Simulation results, some of which are presented, show that the sub-optimum algorithm performs better than the extended Kalman filter algorithm for some non-linear multi-dimensional models with white gaussian disturbance and observation noises. In addition, the performance of the sub-optimum algorithm is almost as good as the Kalman filter algorithm for linear multi-dimensional models with white gaussian noise. |
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