Adaptive Digital Filters

InTech eBooks, 2011

Lino García Morales has graduated in Automatic Control Engineering at Polytechnic Institute "José A. Echeverría". He has received a master's degree in Systems and Communications Networks at

Computers & Electrical Engineering, 1981

This paper describes a special-purpose digital processor for use in performing operations on sampled data. The processor is organized as multiple digital filters which can be grouped in cascade or canonic form. In addition, the weighting coefficients are modifiable in a self-adaptive mode during filter operations to form nonrecursive structures based upon the degree of coherency in two signal input channels. The adaptable system executes an LMS algorithm on a set of filter coefficients to perform, in real-time noise tracking and cancellation. An array with digital AGC, multiplier, interleaved memory and fixed-point processor are described.

1989

Absfracf -Characteristics of the mean-square error surface in adaptive digital filters determine how well a gradient algorithm performs within a given filter structure, i.e., if the surface has steep slopes and contains local minima, a gradient algorithm will have difficulty reaching the global minimum. It is shown how different filter structures of an adaptive filter leads to a change in the characteristics of the corresponding error surface, and consequently, to a change in the corresponding convergence rate and minimum mean-square error.

Journal of University of Shanghai for Science and Technology

An adaptive filter (AF) is a digital filter that has a transfer function that changes based on changes in the surroundings. Adaptive filters can adjust their weights using cost functions similar to a neural network. Implementation of the adaptive filter in hardware allows it to have higher speed (Consumes lesser number of clock cycles) and hence also saving on power. A regular Digital Signal Processor (DSP) may also be employed to do the same but it will never come close to the performance of dedicated hardware. An improvement in this said hardware will directly boost the performance of all use-cases. Simulation of the existing design gives an idea of the current data flow and architecture. Exploring different potential improvements in design and then weighing the outcome gain vs effort to add the functionality is done. An improvement is chosen and implemented. Once it does the intended functionality, It is profiled to see the improvement in performance. A large Filter task is divid...

Resumo— Neste artigo nós discutimos sobre as potencialidades e importância de mecanismos de sele ao de dados em problemas reais. Nós mostramos como filtros adaptativos com seletividade de dados podem ser explorados com a finalidade de melhorar o desempenho de sistemas de comunica oes e canceladores de eco a ustico. Para sistemas de comunica oes propomos algo-ritmos com seletividade de dados para equaliza ao semi-cega em sistemas baseados em OFDM empregando o esquema de modula ao digital QPSK que aumenta o throughput do sistema em 18%, desde que uma razão-sinal-ruído mínima seja garantida. Para o cancelamento de eco a ustico propomos algoritmos com seletividade de dados que operam no domínio da frequência e possibilitam uma redu ao significativa da carga computacional. Os resultados de simula oes mostram que, após a convergência, esses algoritmos atualizam apenas 10% dos coeficients do filtro. Palavras-Chave— Filtros adaptativos com seletividade de da-dos, equaliza ao semi-cega, OFDM...

IEEE Signal Processing Magazine, 2008

I n this issue, "Best of the Web" focuses on adaptive filtering or, more generally, adaptive signal processingthe design of time-varying (adaptive) digital filters that would tune themselves to optimally process nonstationary signals in nonstationary environments. Much of what is found today in adaptive filtering algorithms can be traced back to two seminal articles that were published in 1960. The first article, "Adaptive Switching Circuits," was published by Bernard Widrow and Marcian Hoff, and described the least mean square (LMS) algorithm. This algorithm is widely used in adaptive signal processing, and is the most well-understood approach to training a linear system to minimize the mean square error. Appearing in 1960, the second article, "A New Approach to Linear Filtering and Prediction Problems," was authored by R.E. Kalman and described a recursive solution to the discrete-data linear filtering problem. Since that time, the Kalman filter has been the subject of extensive research and application. The area of adaptive signal processing has had a significant impact on a wide variety of signal processing applications. These include inverse filtering, signal modeling, prediction, channel equalization, echo cancellation, noise cancellation, system identification and control, line enhancement,

Proceedings of International Conference on Neural Networks (ICNN'97)

We have implemented a four-tap adaptive filter in a continuous-time analog VLSI circuit. Since an ideal delay is impossible to implement in continuous-time hardware, we implemented the delay line as a cascade of low-pass filters (called the Gamma filter). Since many years of research in our lab has shown that the Gamma filter outperforms the delay line for a wide range of applications, the Gamma filter should not be considered merely a crude approximation of the ideal delay line. We show measured results from an analog chip that solves the problem of system identification-identifying an unknown linear circuit from its input/output relationship. Furthermore, we believe that a cascade of all-pass filters (called the Laguem filter) may potentially outperform the Gamma filter and we demonstrate a feedforward Laguerre filter still without adaptive weights.

IEEE Transactions on Automatic Control, 1986

Working h p e r s a r e interim reports on work of the international lnstitute for Applied Systems Analysis and have received only limited review. Views or opinions expressed herein do not necessarily represent those of the Institute or of i t s National Member Organizations.

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.

The theoretical framework and principles of designing a new class of highly effective analog-digital computer-aided measurement systems are presented. The proposed approach enables the designers to determine uniquely the optimal structure, parameters and characteristics of measurement systems working in the presence of noises and disturbances as well as to carry out the comprehensive analysis of their work. The approach is based on the new idea of the nonlinear adaptively controlled (smart) sensor. The algorithms for optimal processing of the measured data and estimating unknown parameters of analyzed processes or objects, coupled with the optimal adaptive control of the sensor parameters, are derived. The superiority of measurement systems with adaptively controlled sensors over the conventional ones employing nonadaptive sensors is shown. The main theoretical results and conclusions are verified and confirmed via simulations taking as an example the string-frame accelerometer.