Filtering of Non-Uniformly Multirate Sampled-Data Systems Using the Lifting Technique

This paper uses the lifting technique to derive the lifted state-space models for non-uniformly sampled multirate systems, and transform the obtained models into the canonical ones. Based on the Kalman filtering principle, we derive the state filtering algorithm by minimizing the estimation error covariance matrix and further compute the state estimates of the original systems by using inverse transformation. Finally, an example is given to validate the algorithm proposed.

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