Adaptive Filtering Applications Part 1 doc

Sanchez and Jose Velazquez Chapter 2 Applications of Adaptive Filtering: Recent Advancements in Active Noise Control 21 Akhtar Muhammad Tahir, Mitsuhashi Wataruand Nishihara Akinori Ch

APPLICATIONS 

  Edited by Lino García Morales 

Adaptive Filtering Applications

Edited by Lino García Morales

Published by InTech

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Adaptive Filtering Applications, Edited by Lino García Morales

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Contents

Preface IX

Part 1 Noise and Echo Cancellation 1

Chapter 1 Applications of Adaptive Filtering 3

J Gerardo Avalos, Juan C Sanchez and Jose Velazquez Chapter 2 Applications of Adaptive Filtering:

Recent Advancements in Active Noise Control 21

Akhtar Muhammad Tahir, Mitsuhashi Wataruand Nishihara Akinori Chapter 3 Active Noise Cancellation:

The Unwanted Signal and the Hybrid Solution 49

Edgar Omar López-Caudana Chapter 4 Perceptual Echo Control and Delay Estimation 85

Kirill Sakhnov, Ekaterina Verteletskaya and Boris Simak

Part 2 Medical Applications 121

Chapter 5 Adaptive Noise Removal of ECG Signal Based

On Ensemble Empirical Mode Decomposition 123

Zhao Zhidong, Luo Yi and Lu Qing Chapter 6 Application of Adaptive Noise Cancellation

in Transabdominal Fetal Heart Rate Detection Using Photoplethysmography 141

Kok Beng Gan, Edmond Zahedi and Mohd Alauddin Mohd Ali Chapter 7 Adaptive Filtering by Non-Invasive Vital

Signals Monitoring and Diseases Diagnosis 157

Omar Abdallah and Armin Bolz

VI Contents

Chapter 8 Noise Removal from EEG Signals in

Polisomnographic Records Applying Adaptive Filters in Cascade 173

M Agustina Garcés Correa and Eric Laciar Leber Chapter 9 Fast Extraction of Somatosensory Evoked Potential

Based on Robust Adaptive Filtering 197

Yuexian Zou, Yong Hu and Zhiguo Zhang

Part 3 Communication Systems 211

Chapter 10 A LEO Nano-Satellite Mission

for the Detection of Lightning VHF Sferics 213

Ghulam Jaffer, Hans U Eichelberger, Konrad Schwingenschuh and Otto Koudelka Chapter 11 Adaptive MIMO Channel Estimation

Utilizing Modern Channel Codes 239

Patric Beinschob and Udo Zölzer Chapter 12 An Introduction to ANFIS Based Channel

Equalizers for Mobile Cellular Channels 255

K C Raveendranathan Chapter 13 Adaptive Channel Estimation in Space-Time

Coded MIMO Systems 285

Murilo B Loiola, Renato R Lopes and João M T Romano Chapter 14 Adaptive Filtering for Indoor Localization

using ZIGBEE RSSI and LQI Measurement 305

Sharly Joana Halder, Joon-Goo Park and Wooju Kim

Part 4 Other Applications 325

Chapter 15 Adaptive Filters for Processing Water Level Data 327

Natasa Reljin, Dragoljub Pokrajac and Michael Reiter Chapter 16 Nonlinear Adaptive Filtering

to ForecastAir Pollution 343

Luca Mesin, Fiammetta Orione and Eros Pasero Chapter 17 A Modified Least Mean Square Method

Applied to Frequency Relaying 365

Daniel Barbosa, Renato Machado Monaro, Ricardo A S Fernandes, Denis V Coury and Mário Oleskovicz

Chapter 18 Anti-Multipath Filter with Multiple

Correlators in GNSS Receviers 381

Chung-Liang Chang

Preface

Adaptive filtering is useful in any application where the signals or the modeled system vary over time. The configuration of the system and, in particular, the position where the adaptive processor is placed generate different areas or application fields such as: prediction,  system  identification  and  modeling,  equalization  (deconvolution,  reverse filtering, inverse modeling), cancellation of interference, etc. which are very important 

in  many  disciplines  such  as  control  systems,  communications,  signal  processing, acoustics, voice, sound and image, etc. This book consists of a compendium of applica‐tions in three areas of great interest in scientific research: noise and echo cancellation, medical applications, communications systems and others hardly joined by their het‐erogeneity. There is no a structure and/or algorithm better than other; It all depends on the implementation and the performance target. In all these chapters, each application 

is  a  case  study  with  rigor  that  shows  the  weakness‐strength  of  the  method  used  (in many  cases  compared  with  other  methods),  assesses  its  suitability  and  suggests  new forms and areas of use. The problems are becoming increasingly complex and applica‐tions must be adapted to solve them. The adaptive filters have proven to be useful in these  environments  of  multiple  input/output,  variant‐time  behaviors,  and  long  and complex transfer functions effectively but fundamentally to be still evolving. There are many ʺvariablesʺ to take into  account and how to combine them, optimize them and achieve the desired outcome. This book is a demonstration of this and a small illustra‐tion of everything that is to come. 

Dr. Prof. Lino García Morales 

 Prof. Titular Dpto. Electrónica y Comunicaciones  Coord. Grado en Arte Electrónico y Digital  

Escuela Superior Politécnica  Universidad Europea de Madrid 

Spain 

Part 1

Noise and Echo Cancellation

1

Applications of Adaptive Filtering

J Gerardo Avalos, Juan C Sanchez and Jose Velazquez

National Polytechnic Institute

Mexico

1 Introduction

Owing to the powerful digital signal processors and the development of advanced adaptive algorithms there are a great number of different applications in which adaptive filters are used The number of different applications in which adaptive techniques are being successfully used has increased enormously during the last two decades There is a wide variety of configurations that could be applied in different fields such telecommunications, radar, sonar, video and audio signal processing, noise reduction, between others

The efficiency of the adaptive filters mainly depends on the design technique used and the algorithm of adaptation The adaptive filters can be analogical designs, digital or mixed which show their advantages and disadvantages, for example, the analogical filters are low power consuming and fast response, but they represent offset problems, which affect the operation of the adaptation algorithm (Shoval et al., 1995).The digital filters are offset free and offer an answer of greater precision Also the adaptive filters can be a combination of different types of filters, like single-input or multi-input filters, linear or nonlinear, and finite impulse response FIR or infinite impulse response IIR filters

The adaptation of the filter parameters is based on minimizing the mean squared error between the filter output and a desired signal The most common adaptation algorithms are, Recursive Least Square (RLS), and the Least Mean Square (LMS), where RLS algorithm offers a higher convergence speed compared to the LMS algorithm, but as for computation complexity, the LMS algorithm maintains its advantage Due to the computational simplicity, the LMS algorithm is most commonly used in the design and implementation of integrated adaptive filters The LMS digital algorithm is based on the gradient search according to the equation (1)

w(n + 1) = w(n) + μe(n)x(n) (1) Where w(n) is the weights vector in the instant n, w(n+1) is equal to the weights vector in n+1, x(n) is the input signal simple vector which is stored in the filter delayed line, where e(n) corresponds to the filter’s error, which is the difference between the desired signal and the output filter’s signal, and µ is the filter’s convergence factor The convergence factor µ determines the minimum square average error and the convergence speed This factor is directly proportional to the convergence speed and indirectly proportional to the minimal error Then a convergence speed and minimal error relation is established

The application depends on the adaptive filter configuration used The classical configurations of adaptive filtering are system identification, prediction, noise cancellation,

Adaptive Filtering Applications

4

and inverse modeling The differences between the configurations are given by the way the input, the desired and the output signals are used The main objective of this chapter is to explain the typical configurations and it will focus on recent applications of adaptive filtering that are used in the real world

2 System identification

The system identification is an approach to model an unknown system In this configuration the unknown system is in parallel with an adaptive filter, and both are excited with the same signal When the output MSE is minimized the filter represents the desired model The structure used for adaptive system identification is illustrated in figure 1, where P(z) is

an unknown system to be identified by an adaptive filter W(z) The signal x(n) excites P(z) and W(z), the desired signal d(n) is the unknown system output, minimizing the difference

of output signals y(n) and d(n), the characteristics of P(z) can be determined

Fig 1 Adaptive filter for system identification

The estimation error is given as (2)

e(n)=d(n)-y(n)= ∑L-1l=0[p(l)-w1(n)]x(n-l) (2) Where p(l) is the impulse respond of the unknown plant, By choosing each w1(n) close to each p(l), the error will be minimized For using white noise as the excitation signal, minimizing e(n) will force the w1(n) to approach p(l), that is,

w1(n) ≈ p(l), l = 0, 1, , L – 1 (3) When the difference between the physical system response d(n) and the adaptive model response y(n) has been minimized, the adaptive model approximates P(z) from the input/output viewpoint When the plan is time varying, the adaptive algorithm has the task

of keeping the modelling error small by continually tracking time variations of the plant dynamics

Usually, the input signal is a wideband signal, in order to allow the adaptive filter to converge to a good model of the unknown system If the input signal is a white noise, the best model for the unknown system is a system whose impulse response coincides with the

N + 1 first samples of the unknown system impulse response In the cases where the impulse response of the unknown system is of finite length and the adaptive filter is of sufficient order, the MSE becomes zero if there is no measurement noise (or channel noise)

Applications of Adaptive Filtering 5

In practical applications the measurement noise is unavoidable, and if it is uncorrelated with

the input signal, the expected value of the adaptive-filter coefficients will coincide with the

unknown-system impulse response samples The output error will of course be the

measurement noise (Diniz, 2008) Some real world applications of the system identification

scheme include control systems and seismic exploration

3 Linear predictor

The linear prediction estimates the values of a signal at a future time This model is wide

usually in speech processing applications such as speech coding in cellular telephony,

speech enhancement, and speech recognition In this configuration the desired signal is a

forward version of the adaptive filter input signal When the adaptive algorithm

convergences the filter represents a model for the input signal, this model can be used as a

prediction model The linear prediction system is shown in figure 2

Fig 2 Adaptive filter for linear prediction

The predictor output y(n) is expressed as

Where ∆ is the number of delay samples, so if we are using the LMS algorithm the

coefficients are updated as

Where x(n - ∆) = [x(n - ∆) x(n - ∆ -1) x(n - ∆ - L + l)]T is then delayed reference signal

vector, and e(n) = x(n) – y(n) is the prediction error Proper selection of the prediction delay

∆ allows improved frequency estimation performance for multiple sinusoids in white noise

A typical predictor’s application is in linear prediction coding of speech signals, where the

predictor’s task is to estimate the speech parameters These parameters are part of the

coding information that is transmitted or stored along with other information inherent to

the speech characteristics, such as pitch period, among others

The adaptive signal predictor is also used for adaptive line enhancement (ALE), where the

input signal is a narrowband signal (predictable) added to a wideband signal After

convergence, the predictor output will be an enhanced version of the narrowband signal

Yet another application of the signal predictor is the suppression of narrowband

interference in a wideband signal The input signal, in this case, has the same general

characteristics of the ALE

Adaptive Filtering Applications

6

4 Inverse modeling

The inverse modeling is an application that can be used in the area of channel equalization, for example it is applied in modems to reduce channel distortion resulting from the high speed of data transmission over telephone channels In order to compensate the channel distortion we need to use an equalizer, which is the inverse of the channel’s transfer function

High-speed data transmission through channels with severe distortion can be achieved in several ways, one way is to design the transmit and receive filters so that the combination of filters and channel results in an acceptable error from the combination of intersymbol interference and noise; and the other way is designing an equalizer in the receiver that counteracts the channel distortion The second method is the most commonly used technology for data transmission applications

Figure 3 shows an adaptive channel equalizer, the received signal y(n) is different from the original signal x(n) because it was distorted by the overall channel transfer function C(z), which includes the transmit filter, the transmission medium, and the receive filter

Fig 3 Adaptive Channel equalizer

To recover the original signal x(n), y(n) must be processed using the equalizer W(z), which

is the inverse of the channel’s transfer function C(z) in order to compensate for the channel distortion Therefore the equalizer must be designed by

In practice, the telephone channel is time varying and is unknown in the design stage due to variations in the transmission medium Thus it is needed an adaptive equalizer that provides precise compensation over the time-varying channel The adaptive filter requires the desired signal d(n) for computing the error signal e(n) for the LMS algorithm An adaptive filter requires the desired signal d(n) for computing the error signal e(n) for the LMS algorithm The delayed version of the transmitted signal x(n - Δ) is the desired response for the adaptive equalizer W(z) Since the adaptive filter is located in the receiver, the desired signal generated by the transmitter is not available at the receiver The desired signal may be generated locally in the receiver using two methods During the training stage, the adaptive equalizer coefficients are adjusted by transmitting a short training sequence This known transmitted sequence is also generated in the receiver and is used as the desired signal d(n) for the LMS algorithm