Probability, random processes, and estimation theory for engineers
Probability, random processes, and estimation theory for engineers
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
State-space recursive least-squares: part I
Signal Processing - Special section: New trends and findings in antenna array processing for radar
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Adaptive tracking of linear time-variant systems by extended RLSalgorithms
IEEE Transactions on Signal Processing
State-space recursive least-squares: part I
Signal Processing - Special section: New trends and findings in antenna array processing for radar
| Hi-index | 0.00 |
This paper is a sequel of our earlier development of state-space recursive least squares (SSRLS). Stability and convergence analysis of SSRLS and its steady-state counterpart complete the theoretical framework of this new powerful algorithm. Upper bounds on the forgetting factor that ensure stability of the filter have been derived. Properties like unbiasedness, convergence in mean, mean-square deviation and learning curves have been investigated. The results prove that SSRLS with infinite memory is optimal linear estimator for deterministic signals. The expressions and derivations show that different convergence properties of SSRLS are related to some forms of discrete Riccati or Lyapunov equations. The analysis done in this work would help understand the intricate details and behavior of SSRLS, which could subsequently aid in advanced applications and any further development.