EXPERIMENT VIDigital Filter FIR

Objectives

Student able to explain digital filter FIR and the methods in calculating digital filter FIR coefficients.

Student able to calculate digital filter FIR with available methods using MATLAB

Student able to compare the methods to calculate digital filter FIR coefficient

Student able to design digital filter FIR using MATLAB Student able to design digital filter FIR using DSP Processor

Scope

A. Brief Theory Digital filter FIR (Finite Impulse Response)[1][2][3] is a filter system which impulse response is finite, has linear phase and has no pole which

make it always stable.[1][2][3][4].

Properties of digital filter FIR

Has linear phase response so that it experience no phase distortion, this properties is needed at certain application such as data transmission, biomedical and image processing.

Always stable because no recursive process and limited impulse response.

Round-off noise and coefficient quantization error is smaller because of no feedback.

h(k),k = 0,1,...N-1

x(n)

y(n)

Digital signal input

Digital signal output

N 1y(n) hk xn k ; N filter coef. lengthk 0

N 1H(z) h(k)zkk 0

Digital Signal Processing GuidanceComputer Engineering LaboratoryExperiment VIPage: 1of 10Digital filter FIR specification:

Digital filter FIR design step[4]

Define the characteristics and type of the desired filter

Calculate/define filter coefficient

Choose filter structure

Analyze the effect of coefficient quantization filter and word length limitation from ADC/DAC processor used.

Hardware and/or software implementation Verification/testing

Figure 6.1

START

Specify filter

Calculate filter coefficient

Realize structure

Analysis and solution ofstructure-Recalculate-Respecify-Re

certain word length effect

Hardware and/or software

implementation and testing

STOP

Figure 6.2

Digital Signal Processing GuidanceComputer Engineering LaboratoryExperiment VIPage: 2of 10Calculating filter digital filter FIR method

NoMethodadvantagedisadvantage

require software

linear phase

symmetrical coefficient

optimum filter

Optimum easy to use

program widely available

1Equiripple

(on-line program or

(Parks - McClellan)

application)

able to design LPF, HPF,

BPF, BSF and also multiband

(including special filter such

as Hilbert Transform and

Differentiator)

produce the exact same complicated

design process

frequency response with

requires special

desired response at chosen

2Sampling Frequency

table

frequency sample

can be recursively

implemented

can be designed using because of ideal

simple equationimpulse response

a lot of window functioncut by window

3Windowingavailable to optimizefunction,

frequency responsefrequency

response always

changed

Windowing method:Define filter specification and type Specify ideal filter impulse response (hd(n)) (consult table). Choose suitable window function (w(n)).

Filter coefficient/impulse response is equals to ideal impulse response multiplied by window function

FIR Differensiator dan Hilbert Transformers

FIR Differentiators Is used to apply derivative process to a signal

An ideal differentiator has proportional frequency response to its frequency. Its frequency response:

Hd () j , -

Digital Signal Processing GuidanceComputer Engineering LaboratoryExperiment VIPage: 3of 10 Its impulse response is :

h (n) 1

H

e jn d

d

d2

-

1

hd(0) 0

je jn d

2

-

cosn,- n , n 0

n

Hilbert Transformers An all pass filter which apply 90 phase shift to the input signal

Often used in communication system and signal processing, for example, on single side band modulated signals generation, radar signal processing and speech signal processing Has frequency response:

j,0 H d()

j,- 0 Impulse response:

sin2n

2

2

hd(n)

, n 0

n

0,

n 0

Devices

PC Matlab

III. References

Samuel D.Streans. (2002). Digital Signal Processing with Example in Matlab. Edisi 1. CRC Press. New York. 978-0849310911.

Davis J Defatta ; Joseph G. Lucas ; William S.H. (1995). Digital Signal Processing : A System Design Approach. John Wiley & Son.

Emannuel C Ifeachor. (1993). Digital Signal Processing : A Pratical Approach. Addison-Wesley Publi.

John G Proakis ; Dimitris G.Manolakis. (1992). Digital Signal Procesing, Principles, Algorithms, And Applications.2. Macmilian Publishing. New York.

DIGITAL SIGNAL PROCESSING, Principles, Algorithms, and Applications, Pertemuan 6

Dag Stranneby. (2001). Digital Signal Processing & Applications. Edisi 1. Newnes. Oxford. 0 7506 48112.

Richard G. Lyons. (2004). Understanding Digital Signal Processing. Edisi 2. Prentice Hall. New Jersey. 978-0131089891.

Bob Meddins. (2000). Introduction to Digital Signal Processing. Edisi 1. Newnes. Oxford. 0 7506 5048 6.

Digital Signal Processing GuidanceComputer Engineering LaboratoryExperiment VIPage: 4of 10

Steven W. Smith. (1999). The Scientist and Engineer's Guide to Digital Signal Processing. Edisi 2. California Technical Publishing. California. 0-9660176-7-6.

Interactive digital filter FIR design: http://www- users.cs.york.ac.uk/~fisher/mkfilter/

About digital filter FIR: http://www.bores.com/courses/intro/filters/4_fir.htm

About digital filter FIR (Moving Average): http://www.dspguide.com/ch15.htm

About digital filter FIR: http://www.dspguru.com/info/faqs/firfaq.htm About window sinc filter: http://www.dspguide.com/ch16.htm Matlab HELP

Digital Signal Processing GuidanceComputer Engineering LaboratoryExperiment VIPage: 5of 10IV. Task Description and ProcedureWindowing method

Design digital filter FIR LPF with the specification: sampling frequency 10 KHz, cut-off frequency 2 KHz, transition frequency width 300 Hz, passband ripple 0,03 dB.

Specify windowing function to be used (consult the table at attachment).

Specify order needed for the specification above

Open ToolBox Filter Design & Analysis Tools from Start menu Toolboxes Filter Design & Analysis Tools (FDA Tool)