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
Linear system theory (2nd ed.)
Linear system theory (2nd ed.)
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
State-space recursive least squares: part II
Signal Processing - Special section: New trends and findings in antenna array processing for radar
| Hi-index | 0.00 |
Kalman filter is linear optimal estimator for random signals. We develop state-space RLS that is counterpart of Kalman filter for deterministic signals i.e. there is no process noise but only observation noise. State-space RLS inherits its optimality properties from the standard least squares. It gives excellent tracking performance as compared to existing forms of RLS. A large class of signals can be modeled as outputs of neutrally stable unforced linear systems. State-space RLS is particularly well suited to estimate such signals. The paper commences with batch processing the observations, which is later extended to recursive algorithms. Comparison and equivalence of Kalman filter and state-space RLS become evident during the development of the theory. State-space RLS is expected to become an important tool in estimation theory and adaptive filtering.