Infinite Impulse Response Algorithms Research Articles

Maximum Complex Correntropy Criterion Adaptive IIR Filtering Based on Gauss-Newton Approach

Adaptive infinite impulse response (IIR) filters have received noticeable consideration for its lower computational complexity compared with adaptive finite impulse response (FIR) filters recently. At present, the adaptive IIR algorithms are almost using the well-established stochastic gradient descent method, but the convergence rate is less than satisfactory. In this paper, to pursue the fast convergence speed of Gauss-Newton (GN) method, we present a GN algorithm for adaptive IIR filters with complex coefficients based on the complex correntropy measure. Besides, the steady-state excess mean square error is provided. Simulations are performed to show the eximious performance of this algorithm.

FIR, IIR and Wavelet Algorithms for the Rigorous Filtering of GOCE SGG Data to the GOCE MBW

Gravity field and steady-state Ocean Circulation Explorer (GOCE) data are strongly affected by noise and long-wavelength errors outside the satellite measurement bandwidth (MBW). One of the main goals in utilizing GOCE data for gravity field modeling is the application of filtering techniques that can remove gross errors and reduce low-frequency errors and high-frequency noise while preserving the original signal. This paper aims to present and analyze three filtering strategies used to de-noise the GOCE Level 2 data from long-wavelength correlated errors and noise. These strategies are Finite Impulse Response (FIR), Infinite Impulse Response (IIR), and Wavelet Multi-resolution Analysis (WL), which have been applied to GOCE residual second order derivatives of the gravity potential. Several experiments were performed for each filtering scheme in order to identify the ideal filtering parameters. The outcomes indicate that all the suggested filtering strategies proved to be effective in removing low-frequency errors while preserving the signals in the GOCE MBW, with FIR filtering providing the overall best results.

Interference Cancellation in Aircraft Cockpit by Adaptive Filters

This paper investigates on the development and implementation of adaptive noise cancellation (ANC) algorithm meant for mitigating the high level engine noise in the cockpit of an aircraft, which makes the speech signal unintelligible. Adaptive filters configured as interference canceller have the potential application in mitigating the above interference. A comparative study of Gradient based adaptive Infinite Impulse Response (IIR) algorithm and its modified version is performed using MATLAB simulator in terms of converging speed. From the simulation result the best IIR algorithm is used for implementation in Performance Optimized with Enhanced RISC PC (Power PC) 7448.

Band-Stop Filter Algorithm Research Based on Nano-Displacement Positioning System

This paper proposed the algorithms of infinite impulse response (IIR) band-stop filter and all-pass filter to eliminate the inherent frequency for piezoelectric ceramics and improve the control precision for nanodisplacement positioning system. The IIR algorithm was composed of five steps (such as the determination of normalized frequency, filter orders and transfer functions of analog low-pass filter, analog band-stop filter and digital band-stop filter). Based on the experimental simulation results on the nanodisplacement positioning platform, the butterworth band-stop filter algorithm can achieve the requested filtering effects within 10 orders .

A new framework for convergence analysis and algorithm development of adaptive IIR filters

A parameterization of an adaptive infinite impulse response (IIR) filter's poles is developed, based on balanced realization theory. From this, we develop a local approximation of the actual adapted pole parameters, in which convergence speed is related to a certain eigenvalue spread. This, in turn, is shown to relate to the Hankel singular values of the system to be identified, as well as certain coefficient sensitivity functions of the adapted filter. The local approximation is not restricted to stationary points. At these points, however, it is equivalent to a Hessian approximation, with the benefit of decomposing the Hessian matrix into terms related to the aforementioned singular values and sensitivity functions. The description of the adaptation process by means of the developed approximation leads to a greater understanding of the effects on convergence speed of factors, such as the Hankel singular values of the system, its order, the distribution of its poles, and the choice of adapted parameters. In particular, the use of direct form and lattice parameters are compared in detail. Based on these properties, a new adaptive IIR algorithm with faster convergence and relatively low computational complexity has recently been proposed, which is briefly mentioned. Results also indicate a potential for variable gain algorithms.

Enhancement of measurement accuracy in fiber Bragg grating sensors by using digital signal processing

Wavelength detection accuracy in fiber Bragg grating (FBG) sensors can be increased by use of digital signal processing after photo-detection. Finite impulse response and infinite impulse response algorithms were implemented and used to improve the wavelength detection accuracy of peak detection and time-zero-crossing techniques. The wavelength detection resolution can be improved by an order of magnitude compared with that obtained directly from an optical spectrum analyzer/tunable laser.

Simplified gradient calculation in adaptive IIR lattice filters

A simplified algorithm is presented for computing the gradient in adaptive IIR (infinite impulse response) lattice filters. For a filter with N zeros and N poles, this algorithm requires only order N computations. Several computer simulations were performed to compare the performance of the proposed O(N) formulation and the conventional O(N/sup 2/) formulation, and no difference in their behavior was found. In these simulations the full Hessian and the diagonal Hessian adaptive algorithms were used in a system-identification configuration. It is concluded that the proposed method allows the development of robust adaptive IIR algorithms with a cost proportional to the filter order.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Adaptive IIR algorithms based on high-order statistics

Recursive estimation algorithms are derived for moving-average and autoregressive moving-average processes. These algorithms can be used for adaptive infinite impulse response (IIR) filtering of non-Gaussian processes. The behavior of these algorithms is illustrated by extensive numerical examples.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Efficient modeling of dominant transform components representing time-varying signals

The mixed transform representation of time-varying signals uses partial sets of basis functions from the discrete Fourier transform (DFT) and the Walsh-Hadamard transform. The location, magnitude, and phase of the transform components have to be specified for proper signal reconstruction. A least-squares IIR (infinite impulse response) algorithm, in the transformed domains, which fits each of the retained subsets of the complex transform components accurately, is presented. The IIR function, while characterized by real coefficients about twice the number of the retained complex transform components, carries enough location, magnitude, and phase information for accurate signal reconstruction. To illustrate the technique's accuracy and efficiency, its application to model the DFT part of a voice speech segment is given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An adaptive IIR algorithm with unimodal performance surfaces

The authors develop an adaptive IIR (infinite-impulse response) algorithm by means of least squares inverses; the algorithm also guarantees the resulting IIR system to be stable. This IIR algorithm consists of two subalgorithms, i.e. the all-pole Q/sub N/(z) and all-zero P/sub N/(z) algorithms. Both are with quadratic performance surfaces and stable. The iterations for Q/sub N/(z) and P/sub N/(z) can be executed simultaneously or alternatively. The all-pole part is independent of P/sub N/(z), but the all-zero part depends on Q/sub N/(z) at each iteration. In many cases, the all-pole model itself is a fairly good approximation of an IIR system. >