Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Nonlinear Control Systems
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
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 with adaptive memory
Signal Processing
Adaptive tracking of linear time-variant systems by extended RLSalgorithms
IEEE Transactions on Signal Processing
Comparison of RLS, LMS, and sign algorithms for tracking randomlytime-varying channels
IEEE Transactions on Signal Processing
Multi-innovation stochastic gradient algorithms for multi-input multi-output systems
Digital Signal Processing
An alternative FIR filter for state estimation in discrete-time systems
Digital Signal Processing
Gradient based and least-squares based iterative identification methods for OE and OEMA systems
Digital Signal Processing
Input--output data filtering based recursive least squares identification for CARARMA systems
Digital Signal Processing
Several multi-innovation identification methods
Digital Signal Processing
Identification methods for Hammerstein nonlinear systems
Digital Signal Processing
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In this paper, we present a generalized form of the well-known least mean square (LMS) filter. The proposed filter incorporates linear time-varying state-space model of the underlying environment and hence is termed as state-space LMS (SSLMS). This attribute results in marked improvement in its tracking performance over the standard LMS. Furthermore, the use of SSLMS in state estimation in control systems is straightforward. Overall performance of SSLMS, however, depends on factors like model uncertainty and time-varying nature of the problem. SSLMS with adaptive memory, having time-varying step-size parameter, provides solutions to such cases. The step-size parameter is iteratively tuned by stochastic gradient method so as to minimize the mean square value of the prediction error. Different computer simulations demonstrate the ability of the algorithms suggested in this paper. A detailed study of computational complexities of the proposed algorithms is carried out at the end.