Sufficient stability bounds for slowly varying direct-form recursive linear filters and their applications in adaptive IIR filters

Journal ArticleAbstract-This correspondence derives a sufficient time-varying bound on the maximum variation of the coefficients of an exponentially stable time-varying direct-form homogeneous linear recursive filter. The stability bound is less conservative than all previously derived bounds for time-varying IIR systems. The bound is then applied to control the step size of output-error adaptive IIR filters to achieve bounded-input bounded-output (BIBO) stability of the adaptive filter. Experimental results that demonstrate the good stability characteristics of the resulting algorithms are included. This correspondence also contains comparisons with other competing output-error adaptive IIR filters. The results indicate that the stabilized method possesses better convergence behavior than other competing techniques

ADAPTIVE AND NONLINEAR SIGNAL PROCESSING

1996/1997X Ciclo1967Versione digitalizzata della tesi di dottorato cartacea

A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals

Journal ArticleAbstract This paper presents an algorithm that adapts the parameters of a Hammerstein system model. Hammerstein systems are nonlinear systems that contain a static nonlinearity cascaded with a linear system. In this work, the static nonlinearity is modeled using a polynomial system and the linear filter that follows the nonlinerity is an infinite impulse response system. The adaptation of the nonlinear components is enhanced in the algorithm by orthogonalizing the inputs to the coefficients of the polynomial system. The linear system is implemented as a recursive higher-order filter. The step sizes associated with the recursive components are constrained in such a way as to guarantee bounded-input, bounded-output stability of the overall system. Experimental results included in the paper show that the algorithm performs well and always converges to the global minimum of the input signal is white

Improved Distributed Estimation Method for Environmental\ud time-variant Physical variables in Static Sensor Networks

In this paper, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time-variant physical variables. The main contribution of this work is that the algorithm in [1]-[3] has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimized, thereby improving the filter design considerably\ud and characterizing the performance limit of the filter, and thereby tracking a time-varying signal. Moreover, certain parameter optimization is alleviated with the application of a particular finite impulse response (FIR) filter. Simulation results are showing the effectiveness of the developed estimation algorithm

Digital processing of signals in the presence of inter-symbol interference and additive noise

Imperial Users onl

The reduced order model problem in distributed parameter systems adaptive identification and control

The research concerning the reduced order model problem in distributed parameter systems is reported. The adaptive control strategy was chosen for investigation in the annular momentum control device. It is noted, that if there is no observation spill over, and no model errors, an indirect adaptive control strategy can be globally stable. Recent publications concerning adaptive control are included