Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations
This work addresses system identification challenges in signal processing or control systems, but it appears incremental as it builds on existing recursive total least-squares algorithms with improvements in efficiency and stability.
The paper tackles the problem of errors-in-variables system identification by developing a recursive total least-squares algorithm called DCD-RTLS, which uses the inverse power method and dichotomous coordinate-descent iterations to outperform previous methods with reduced computational complexity, achieving asymptotic unbiasedness and stability as verified by simulations.
We develop a recursive total least-squares (RTLS) algorithm for errors-in-variables system identification utilizing the inverse power method and the dichotomous coordinate-descent (DCD) iterations. The proposed algorithm, called DCD-RTLS, outperforms the previously-proposed RTLS algorithms, which are based on the line-search method, with reduced computational complexity. We perform a comprehensive analysis of the DCD-RTLS algorithm and show that it is asymptotically unbiased as well as being stable in the mean. We also find a lower bound for the forgetting factor that ensures mean-square stability of the algorithm and calculate the theoretical steady-state mean-square deviation (MSD). We verify the effectiveness of the proposed algorithm and the accuracy of the predicted steady-state MSD via simulations.