Adaptive Infinite Impulse Response Filters Research Articles

A recursive maximum likelihood algorithm for ARMA line enhancement

Optimal filtering of narrow-band signals corrupted by noise requires an infinite impulse response (IIR) filter. A recursive maximum likelihood algorithm is proposed for adaptively adjusting the coefficients of an IIR line enhancer. The performance of this adaptive IIR filter is analyzed and compared to that of a finite impulse response filter. Preliminary simulation results are presented to illustrate the behavior of the proposed algorithm under various signal-to-noise conditions.

Implementation of Adaptive Filters Using Residue Number Systems

Implementation of adaptive infinite impulse response (IIR) digital filters using residue number is presented. Earlier, Jenkins and his co-workers have considered implementation of IIR filters with fixed coefficients, and Soderstrand has implemented an adaptive finite impulse response (FIR) filter. Here, we extend the earlier work to the adaptive IIR filter. An output and equation error adaptive identification algorithm which has been shown by Johnson to be convergent at low convergence rates is used. Residue arithmetic decomposes the arithmetic operations which are well suited for high speed ROM table look-up, for computations on parallel microcomputers, or for special computer architectures involving only shift registers and adders.

System identification techniques for adaptive signal processing

Many problems in adaptive filtering can be approached from the point of view of system identification. The recursive maximum likelihood algorithm is proposed for estimating the parameters of the signal model. The parameter estimates are then used to form an adaptive infinite impulse response filter. Several examples are discussed including: adaptive line enhancement, adaptive deconvolution, adaptive noise cancelling, and adaptive time delay estimation.