FREQUENCY RESPONSE IDENTIFICATION USING EXACT ADAPTIVE FREQUENCY SAMPLING FILTERS (original) (raw)

Adaptive frequency sampling filters

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981

We present two new structures for adaptive filters based on the idea of frequency sampling filters and gradient based estimation algorithms. These filters have a finite impulse response (FIR) and can be thought of as attempting to approximate a desired frequency response at given points on the unit circle. The filters operate in real time with no batch processing of signals as is the case when using the discrete Fourier transform. They result in a marked reduction in dimension of the time-domain problem of fitting an Nth-order FIR transversal filter to a collection of length 2 transversal filters and further to a collection of N scalar filters. The advantages of this are then discussed.

Adaptive system identification using time-varying Fourier transform

2009 Second International Conference on the Applications of Digital Information and Web Technologies, 2009

In this paper, we introduce a time-varying shorttime Fourier transform (TV-STFT) for representing discrete signals. We derive an explicit condition for perfect reconstruction using time-varying analysis and synthesis windows. Based on the derived representation, we propose an adaptive algorithm that controls the length of the analysis window to achieve a lower mean-square error (MSE) at each iteration. When compared to the conventional multiplicative transfer function approach with a fixed length analysis window, the resulting algorithm achieves faster convergence without compromising for higher steady state MSE. Experimental results demonstrate the effectiveness of the proposed approach.

Adaptive frequency selective filters

Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, 1993

The theory and performance of adaptrve frequency selecttve filters as examined. The frequency sampling filter is a realazaiaon of a FIR filter as the cascade of an all-zero FIR filter with a bank of IIR digital resonators. The result of such a realazatzon i s that each coeficzent can be darectly identified with an amplitude of the transfer functaon at a particular frequency. The update method as the LMS algorathm, wath the desired signal as a delayed version of the znput. A dzscussion of the applacataon of the adaptive frequency samplzng filters t o sub-band codzng as Included.

Comparative Analysis of various Adaptive Filtering Algorithms for Adaptive System Identification

System identification is one of the most interesting applications for adaptive filters, for this dissertation provides a comparison of LMS, VSSLMS,NLMS and TDLMS adaptive algorithms. This process provided the best suitable algorithm for usage in adaptive filters for system identification. This technique Based on the error signal, where filter's coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. Its applications include echo cancellation, channel equalization, interference cancellation, and so forth. Simulation results show that the proposed algorithms outperform the standard NLMS and TDLMS algorithms in both convergence rate and steady-state performance for sparse systems identification.

A characterization of a single-trial adaptive filter and its implementation in the frequency domain

Electroencephalography and Clinical Neurophysiology, 1989

A single-trial adaptive filter (SAF) was implemented in the frequency domain (FDAF) by using the Fast Fourier Transform. The FDAF is significantly more efficient than the SAF. In the data presented the FDAF ran approximately 2 times faster than the SAF. For time series containing larger numbers of data points (n) the efficiency of the calculation will increase on the order of N/Ln(N). The FDAF was tested under a variety of conditions to determine the limits of its usefulness. Pre-fihering the data was found to be necessary to prevent the FDAF from lining up on high frequency activity not related to the signal. The importance of minimizing the amount of low frequency noise was emphasized since it adversely affected the performance of the FDAF and was difficult to filter. The single-trial latencies predicted by the FDAF were much more sensitive to increasing noise than the final wave form. In th~ absence of excessive low frequency noise a negative exponential relationship was found between the mean error in latency prediction and the SNR estimate. Since the SAF technique is also used to determine signal latency in single sweep data the SNR estimate can be a useful test to determine if the FDAF is locating the signal correctly or merely amplifying chance regularities in noisy data.

Adaptive IIR filtering algorithms for system identification: a general framework

IEEE Transactions on Education, 1995

A6strmt-Adaptive IIR (infinite impulse response) filters are particularly beneficial in modeling real systems because they require lower computational complexity and can model sharp resonances more efficiently as compared to the FIR (finite impulse response) counterparts. Unfortunately, a number of drawbacks are associated with adaptive IIR filtering algorithms that have prevented their widespread use, such as: Convergence to biased or local minimum solutions, requirement of stability monitoring, and slow convergence. Most of the recent research effort on this field is aimed at overcoming some of the above mentioned drawbacks. In this paper, a number of known adaptive IIR filtering algorithms are presented using a unifying framework that is useful to interrelate the algorithms and to derive their properties. Special attention is given to issues such as the motivation to derive each algorithm and the properties of the solution after convergence. Several computer simulations are included in order to verify the predicted performance of the algorithms. Index Tenns-adaptive filters, adaptive algorithms.

A fast filter-bank for adaptive Fourier analysis

IEEE Transactions on Instrumentation and Measurement, 1998

Adaptive Fourier analyzers have been developed for measuring periodic signals with unknown or changing fundamental frequency. Typical applications are vibration measurements and active noise control related to rotating machinery and calibration equipment that can avoid the changes of the line frequency by adaptation. Higher frequency applications have limitations since the computational complexity of these analyzers are relatively high as the number of the harmonic components to be measured (or suppressed) is usually above 50.

IJERT-A Survey on the Different Adaptive Algorithms Used In Adaptive Filters

International Journal of Engineering Research and Technology (IJERT), 2012

https://www.ijert.org/a-survey-on-the-different-adaptive-algorithms-used-in-adaptive-filters https://www.ijert.org/research/a-survey-on-the-different-adaptive-algorithms-used-in-adaptive-filters-IJERTV1IS9361.pdf Adaptive filters finds application in various fields which includes echo cancellers, noise cancellation, system identification etc. There are many algorithms available and the choice of a particular type of algorithm is dependent on the requirement of the algorithms in the particular environment. Among the various types of algorithms, LMS is very commonly used because of its simplicity. In this paper a comparison of the variants of LMS algorithms with respect to their convergence behavior, tracking capability, robustness, computational complexity, steady state error is made .

Unconstrained Frequency-Domain Adaptive Filter

on the "overlap-save" technique used in frequency-domain fiitering. The proposed algorithm converges to the Wiener solution without the timedomain constraint on the impulse response as proposed by Ferrara.

A fast frequency-domain adaptive algorithm

Proceedings of the IEEE, 1988

In accordance with a decision of the PROCEEDINGS OF THE IEEE Editorial Board, the "Proceedings Letters" section will be discontinued in 1988. As a consequence, technical letters postmarked after October 31, 1987 will not be considered for publication.