Lecture 2: February 27, 2007 - PowerPoint PPT Presentation

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Lecture 2: February 27, 2007

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  1. Lecture 2: February 27, 2007 Topics: 1. Introduction to Digital Filters 2. Linear Phase FIR Digital Filter. Introduction 3. Linear-Phase FIR Digital Filter Design: Window (Windowing) Method

  2. Lecture 2: February 27, 2007 Topic: 1. Introduction to Digital Filters • basic terminology and definitions: filtering, filter, analogue filtering, digital/discrete-time filtering and filters, • frequency-selective filter classification, • basic parameter specification for filter design.

  3. Lecture 2: February 27, 2007 Topic: 2. Linear Phase FIR Digital Filter. Introduction • advantages and disadvantages of linear phase FIR digital filters, • linear phase conditions for FIR filters, • four groups/kinds of linear phase FIR digital filters.

  4. Lecture 2: February 27, 2007 Topic: 3. Linear-Phase FIR Digital Filter Design: Window (Windowing) Method • basic principles and algorithms, • method description in time- and frequency-domain, • Example A.: FIR filter design-rectangular window application, • Gibbs’ phenomenon and different windowing applications, • Example B.: FIR filter design at different window applications.

  5. 2. Introduction to Digital Filters 2.1. Definitions of Basic Terms

  6. Filtering: process of extraction of desired signal from noise Filter:system performing filtering Analogue filtering:filtering performed on continuous-time signals and yields continuous-time signals Digital/discrete-time filtering:filtering performed on digital/discrete-time signals and yields digital/ discrete-time signals

  7. Examples of filtering applications A. Noise suppression • Received radio signals. • Signals received by imaging sensors, such as television cameras or infrared imaging devices. • Electrical signals measured from the human body (such as brain, heart or neurological signals).

  8. B. Enhancement of selected frequency range • Treble and bass control or graphic equalizers in audio systems. • Enhancement of edges in image processing. C. Bandwith limiting • Bandwidth limiting as a means of aliasing prevention in sampling. • Application in FDMA communication systems (Frequency Division Multiple Access - FDMA).

  9. D. Removal or attenuation of specific frequencies • Blocking of the DC component of a signal. • Attenuation of interference from powerline (50 Hz).

  10. Differentiation: Integration: Hilbert transform: E. Special operations

  11. Ideal magnitude frequency response 2.2. Filter Specifications 2.2.1. Ideal Filters Low-Pass Filters:Low-pass filters are designed to pass low frequencies, from zero to a certain cut off frequency and to block high frequencies.

  12. Ideal magnitude frequency response 2.2. Filter Specifications 2.2.1. Ideal Filters Low-Pass Filters:

  13. Ideal magnitude frequency response High-Pass Filters:High-pass filters are designed to pass high frequencies, from a certain cut off frequency to , and to block low frequencies.

  14. Ideal magnitude frequency response High-Pass Filters:

  15. Ideal magnitude frequency response Band-Pass Filters:Band-pass filters are designed to pass a certain frequency range, which does not include zero, and to block other frequencies.

  16. Ideal magnitude frequency response Band-Pass Filters:

  17. Ideal magnitude frequency response Band-Stop Filters:Band-stop filters are designed to block a certain frequency range, which does not include zero, and to pass other frequencies.

  18. Ideal magnitude frequency response Band-Stop Filters:

  19. Multiband Filters:This type of filters generalizes the previous four types of filters in that it allows for different gains or attenuations in different frequency bands. A piecewise –constant multiband filter is characterized by the following parameters: Possible ideal magnitude frequency response

  20. A division of the frequency range to a finite union of intervals. Some of these intervals are pass bands, some are stop bands, and the remaining can be transition bands. • A desired gain and a permitted tolerance for each pass band. • An attenuation threshold for each stop band. Possible ideal magnitude frequency response

  21. A. Comments on phase response:The phase response of ideal filters is linear: B. Comments on group delay function:Group delay function of ideal filters is constant: C. Note: It will be proved for linear phase FIR filters:

  22. Example: All-Pass Filters:A filter is called all-pass if its magnitude response is identically a positive constant ( ) at all frequencies. The phase response of an all-pass filter is not restricted and is allowed to vary arbitrarily as a function of the frequency. In general, a rational filter is all-pass if only if it has the same number of poles and zeros (including multiplicities), and each zero is the conjugate inverse of a corresponding pole: zk=1/pk.

  23. Ideal normalized frequency response Differentiator:The ideal frequency response of a digital differentiator is

  24. Ideal normalized frequency response Hilbert Transformer:The frequency response of an ideal Hilbert transformer is

  25. 2.2.2. Practical (Real, Causal) Filters: Description by a Set of Parameters • pass band (bands), • stop band (bands), • transition band (bands), • pass band cut off frequency/frequencies, • stop band cut off frequency/frequencies, • pass band ripple/ripples, • stop band ripple/attenuation (ripples/attenuations).

  26. pass band stop bands transition bands

  27. pass-band ripple stop-band ripple (attenuation)

  28. 3. Linear PhaseFIR Digital Filter. Introduction 3.1. Advantages and Disadvantages of Linear Phase FIR Digital Filters

  29. Mathematical model of a causal FIR digital filter: FIR digital filter has a finite number of non-zero coefficients of its impulse response: Digital FIR filters cannot be derived from analogue filters, since causal analogue filters cannot have a finite impulse response. In many digital signal processing applications, FIR filters are preferred over their IIR counterparts.

  30. The advantages of FIR filters (1): • FIR filters with exactly linear phase can be easily designed. This simplifies the approximation problem, in many cases, when one is only interested in designing of a filter that approximates an arbitrary magnitude response. Linear phase filters are important for applications where frequency dispersion due to nonlinear phase is harmful (e.g. speech processing and data transmission). • There are computationally efficient realizations for implementing FIR filters. These include both non-recursive and recursive realizations.

  31. The advantages of FIR filters (2): • FIR filters realized non-recursively are inherently stable and free of limit cycle oscillations when implemented on a finite-word length digital system. • The output noise due to multiplication round off errors in FIR filters is usually very low and the sensitivity to variations in the filter coefficients is also low. • Excellent design methods are available for various kinds of FIR filters with arbitrary specifications.

  32. The disadvantages of FIR filters: • The relative computational complexity of FIR filter is higher than that of IIR filters. This situation can be met especially in applications demanding narrow transition bands or if it is required to approximate sharp cut off frequency. The cost of implementation of an FIR filter can be reduced e.g. by using multiplier-efficient realizations, fast convolution algorithms and multirate filtering. • The group delay function of linear phase FIR filters need not always be an integer number of samples.

  33. 3.2. Frequency Response of Linear Phase FIR Digital Filters FIR filter of length M :

  34. A. Symmetrical impulse response: B. Antisymmetrical impulse response: It will be shown that the linear phase condition is obtained by imposing symmetry conditions on the impulse response of the filter. In particular, we consider two different symmetry conditions for h(k): The length of the impulse responseof the FIR filter (M)can be even or odd. Then, the four cases of linear phase FIR filters can be obtained.

  35. 3.2.1. Symmetrical Impulse Response, M:Even h(7)=h(8) h(2)=h(13) h(1)=h(14) h(0)=h(15)

  36. Example:M=4 (even), symmetrical impulse response

  37. End.

  38. Here, the real-valued frequency response is given by

  39. We observe that the phase response is a linear function of provided that is positive or negative. When changes the sign from positive to negative (or vice versa), the phase undergoes an abrupt change of radians. If these phase changes occur outside the pass-band of the filter we do not care, since the desired signal passing through the filter has no frequency content in the stop-band.

  40. “h(7)=h(7)” h(1)=h(13) h(6)=h(8) h(0)=h(14) 3.2.2. Symmetrical Impulse Response, M:Odd

  41. Example:M=5 (odd), symmetrical impulse response

  42. the real-valued frequency response

  43. h(1)=-h(14) h(0)=-h(15) h(7)=-h(8) 3.2.3. Antisymmetrical Impulse Response, M:Even

  44. Example:M=4 (even), antisymmetrical impulse response

  45. the real-valued frequency response

  46. ! ! Low-pass and band-stop filters cannot possess an antisymetrical impulse response because Here, the real-valued frequency response is given by

  47. 3.2.4. Antisymmetrical Impulse Response, M:Odd h(1)=-h(15) ! h(8)=-h(8)=0 h(0)=-h(16) h(7)=-h(9)