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
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
State-space recursive least squares: part II
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
H∞ optimality of the LMS algorithm
IEEE Transactions on Signal Processing
State-space recursive least squares: part II
Signal Processing - Special section: New trends and findings in antenna array processing for radar
State-space recursive least-squares with adaptive memory
Signal Processing
Digital Signal Processing
Hardware/software co-design of a real-time kernel based tracking system
Journal of Systems Architecture: the EUROMICRO Journal
Gauss-Newton filtering incorporating Levenberg-Marquardt methods for tracking
Digital Signal Processing
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In this paper we present state-space recursive least-squares (SSRLS) filter. This algorithm is a new addition to the family of RLS filters. We cover core topics like batch processing, recursive updates, initialization and steady state solution, etc. SSRLS is very well-suited to estimate a wide class of deterministic signals corrupted by observation noise. This new filter exhibits excellent tracking performance by overcoming some of the limitations of the standard RLS algorithm. With its state-space formulation and sound mathematical basis, SSRLS is expected to become an important tool in estimation theory, adaptive filtering and control systems.