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)