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Digital Filter Structures • Is it practical to use the convolutional sum to implement LTI systems? y [n ] k h [k ] x [n k ] • FIR: Yes. y [n ] k 0 h [k ] x [n k ] N • IIR: No it is not practical because the impulse response is of infinite length. But it can be implemented by direct computation of difference equation y[n] kN1 d k y[n k ] kM0 pk x[n k ] Block Diagram Representation • For the implementation of an LTI digital filter, the input-output relationship must be described by a valid computational algorithm • To illustrate what we mean by a computational algorithm, consider the causal first-order LTI digital filter shown below • The filter is described by the diff. eqn.: y[n] d1 y[n 1] p0 x[n] p1x[n 1] • Using the above equation we can compute y[n] if the initial condition (y[-1]) is known y[0] d1 y[1] p0 x[0] p1x[1] • y[1] d1 y[0] p0 x[1] p1x[0] y[2] d1 y[1] p0 x[2] p1x[1] .. . • Each step of the calculation requires a knowledge of the previously calculated value of the output sample (delayed value of the output), the present value of the input sample, and the previous value of the input sample (delayed value of the input) As a result, the first-order difference equation can be interpreted as a valid computational algorithm Basic Building Blocks • The computational algorithm of an LTI digital filter can be conveniently represented in block diagram form using the basic building blocks shown below x[n] y[n] A w[n] Multiplier Adder x[n] z 1 Unit delay y[n] x[n] x[n] x[n] y[n] x[n] Pick-off node Analysis of Block Diagrams • Example - Consider the single-loop feedback structure shown below • The output E(z) of the adder is E ( z ) X ( z ) G2 ( z )Y ( z ) • But from the figure Y ( z ) G1 ( z ) E ( z ) • Eliminating E(z) from the previous two equations we arrive at [1 G1 ( z )G2 ( z )]Y ( z ) G1 ( z ) X ( z ) H ( z) Y ( z) G1 ( z ) X ( z ) 1 G1 ( z )G2 ( z ) The Delay-Free Loop Problem • For physical realizability of the digital filter structure, it is necessary that the block diagram representation contains no delay-free loops • To illustrate the delay-free loop problem consider the structure below • Analysis of this structure yields u[n] w[n] y[n] y[n] B(v[n] Au[n]) y[n] Bv[n] A( w[n] y[n]) To determine the current value of y[n] you need to know the value of y[n] ! This is physically impossible to achieve Canonic and Noncanonic Structures • A digital filter structure is said to be canonic if the number of delays in the block diagram representation = the order of the transfer function • Otherwise, it is a noncanonic structure • Example y[n] d1 y[n 1] p0 x[n] p1x[n 1] No. of Delays in the block diagram = 2 Order of the transfer function = 1 Noncanonic Equivalent Structures • • Two digital filter structures are defined to be equivalent if they have the same transfer function A simple way to generate equivalent structures is using Transpose Operation Transpose Operation (1) Reverse all paths (2) Replace pick-off nodes by adders, and vice versa (3) Interchange the input and output nodes Equivalent Structures • Question: Why study different structures although they have the same transfer function? • Assuming infinite precision arithmetic, any given realization of a digital filter behaves identically to any other equivalent structure. • However, in practice, due to the finite wordlength limitations, a specific realization behaves totally differently from its other equivalent realizations • There are literally an infinite number of equivalent structures realizing the same transfer function • It is thus impossible to develop all equivalent realizations. • One way to find a good structure is to determine a large number of equivalent structures, analyze the finite wordlength effects in each case, and select the one showing the least quantization effects • In this course we restrict our attention to a discussion of some commonly used structures. Basic FIR Digital Filter Structures • A causal FIR filter of order N is characterized by a transfer function H(z) given by N n H ( z ) n 0 h[n]z • In the time-domain the input-output relation of the above FIR filter is given by N y[n] k 0 h[k ]x[n k ] • An FIR filter of order N is characterized by N+1 coefficients and, in general, require N+1 multipliers and N adders • Structures in which the multiplier coefficients are equal to the coefficients of the transfer function are called direct form structures Direct Form FIR Digital Filter Structures • A direct form realization of an FIR filter can be readily developed from the convolution sum description as indicated below for N = 4 • An analysis of this structure yields y [n ] h [0]x [n ] h [1]x [n 1] h [2]x [n 2] h [3]x [n 3] h [4]x [n 4] which is precisely of the form of the convolution sum description • The direct form structure is also known as a tapped delay line or a transversal filter Direct Form FIR Digital Filter Structures • The transpose of the direct form structure shown earlier is indicated below • Both direct form structures are canonic with respect to delays Cascade Form FIR Digital Filter Structures • A higher-order FIR transfer function can also be realized as a cascade of secondorder FIR sections and possibly a first-order section • We express H(z) as H ( z ) h[0]kK1(1 1k z 1 2k z 2 ) where K N if N is even, and K N21 if N is 2 odd, with 2 K 0 Cascade Form FIR Digital Filter Structures • A cascade realization for N = 6 is shown below • Each second-order section in the above structure can also be realized in the transposed direct form Polyphase FIR Structures • Example: consider a causal FIR transfer function H(z) with N = 8: H (z ) h [0] h [1]z 1 h [2]z 2 h [3]z 3 h [4]z 4 h [5]z 5 h [6]z 6 h [7]z 7 h [8]z 8 • H(z) can be expressed as a sum of two terms, with one term containing the even-indexed coefficients and the other containing the odd-indexed coeff.: H (z ) (h[0] h[2]z 2 h[4]z 4 h[6]z 6 h[8]z 8 ) (h[1]z 1 h[3]z 3 h[5]z 5 h[7]z 7 ) H (z ) (h[0] h[2]z 2 h[4]z 4 h[6]z 6 h[8]z 8 ) z 1 (h[1] h[3]z 2 h[5]z 4 h[7]z 6 ) H ( z ) E0 ( z 2 ) z 1E1( z 2 ) Polyphase Decomposition Polyphase FIR Structures • In a similar manner, by grouping the terms in the original expression for H(z), we can re-express it in the form H ( z ) E0 ( z 3 ) z 1E1( z 3 ) z 2 E2 ( z 3 ) E0 ( z ) h[0] h[3]z 1 h[6]z 2 E1( z ) h[1] h[4]z 1 h[7]z 2 E2 ( z ) h[2] h[5]z 1 h[8]z 2 • The subfilters (the E filters) in the polyphase realization of an FIR transfer function are also FIR filters and can be realized using any methods described so far • The above polyphase realizations are non-canonic. To obtain a canonic realization of the overall structure, the delays in all subfilters must be shared Linear-Phase FIR Structures • The symmetry (or antisymmetry) property of a linear-phase FIR filter can be exploited to reduce the number of multipliers into almost half of that in the direct form implementations • Consider a length-7 Type-I FIR transfer function with a symmetric impulse response: H ( z ) h[0] h[1]z 1 h[2]z 2 h[3]z 3 h[2]z 4 h[1]z 5 h[0]z 6 • Rewriting H(z) in the form H ( z ) h[0](1 z 6 ) h[1]( z 1 z 5 ) h[2]( z 2 z 4 ) h[3]z 3 we obtain the linear phase realization • In this example: • No. of Multipliers of Linear-Phase realization = 4 • No. of Multipliers of Direct-Form realization = 7 Linear-Phase FIR Structures • A similar decomposition can be applied to a Type 2 FIR transfer function • For example, a length-8 Type 2 FIR transfer function can be expressed as 7 1 6 H ( z ) h[0](1 z ) h[1]( z z ) 2 5 3 4 h[2]( z z ) h[3]( z z ) • The corresponding realization is shown on the next slide Copyright © 2001, S. K. Mitra Linear-Phase FIR Structures • Note: The Type 1 linear-phase structure for a length-7 FIR filter requires 4 multipliers, whereas a direct form realization requires 7 multipliers Copyright © 2001, S. K. Mitra Basic IIR Digital Filter Structures • • • • The causal IIR digital filters we are concerned with in this course are characterized by a real rational transfer function of z-1 or, equivalently by a constant coefficient difference equation From the difference equation representation, it can be seen that the realization of the causal IIR digital filters requires some form of feedback An N-th order IIR digital transfer function is characterized by 2N+1 unique coefficients, and in general, requires 2N+1 multipliers and 2N two-input adders for implementation Direct form IIR filters: Filter structures in which the multiplier coefficients are equal to the coefficients of the transfer function Basic IIR Digital Filter Structures • An N-th order IIR digital transfer function is characterized by 2N+1 unique coefficients, and in general, requires 2N+1 multipliers and 2N two-input adders for implementation • Direct form IIR filters: Filter structures in which the multiplier coefficients are equal to the coefficients of the transfer function • Consider for simplicity a 3rd-order IIR filter with a transfer function P( z ) p0 p1z 1 p2 z 2 p3 z 3 H ( z) D( z ) 1 d1z 1 d 2 z 2 d3 z 3 Direct Form IIR Digital Filter Structures P( z ) p0 p1z 1 p2 z 2 p3 z 3 H ( z) D( z ) 1 d1z 1 d 2 z 2 d3 z 3 Direct Form I Note: The direct form I structure is noncanonic as it employs 6 delays to realize a 3rd-order transfer function Transposed Direct Form I Direct Form IIR Digital Filter Structures • Various other noncanonic direct form structures can be derived by simple block diagram manipulations as shown below Sharing of all delays reduces the total number of delays to 3 resulting in a canonic realization Direct Form IIR Digital Filter Structures Direct Form II Transposed Direct Form II Cascade Form IIR Digital Filter Structures • By expressing the numerator and the denominator polynomials of the transfer function as a product of polynomials of lower degree, a digital filter can be realized as a cascade of low-order filter sections • Consider, for example, H(z) = P(z)/D(z) expressed as H (z ) = P (z )P2 (z )P3 (z ) P (z ) = 1 D(z ) D1(z )D2 (z )D3 (z ) • Examples of cascade realizations obtained by different pole-zero pairings or different ordering of sections are shown below • There are 36 different structures. Due to finite wordlength effects, each such cascade realization behaves differently from others Cascade Form IIR Digital Filter Structures • Usually, the polynomials are factored into a product of 1st order and 2nd order polynomials: 1 1k z 1 2 k z 2 H ( z ) p0 1 2 k 1 1k z 2 k z In the above, for a first-order factor 2k 2k 0 • Question: Why prefer 1st order and 2nd order sections? • It’s easier to check and ensure stability 1st Order Section transfer func. To be stable and causal 2nd Order 1 cz 1 1 az 1 bz 2 c 1 a 2 AND a 1 b Cascade Form IIR Digital Filter Structures • Example Consider the 3rd-order transfer function 1 11z 1 1 12 z 1 22 z 2 H ( z ) p0 1 1 2 111z 1 12 z 22 z • One possible realization is shown below Cascade Form IIR Digital Filter Structures • Example - Direct form II and cascade form realizations of H ( z) 0.44 z 1 0.362 z 2 0.02 z 3 1 0.4 z 1 0.18 z 2 0.2 z 3 10.8 z 0.5 z 10.4 z 1 2 1 0.44 0.362 z 1 0.02 z 2 z 1 Direct form II Cascade form of a 1st order and 2nd order sections Parallel Form IIR Digital Filter Structures • A partial-fraction expansion of the transfer function in z-1 leads to the parallel form structure • Assuming simple poles, the transfer function H(z) can be expressed as 0 k 1k z 1 H ( z) 0 1 2 1 z z 1k 2k k • In the above for a real pole 2k 1k 0 Parallel form Parallel Form IIR Digital Filter Structures • Example – Find a parallel form I realization: H ( z) 0.44 z 1 0.362 z 2 0.02 z 3 1 0.4 z 1 0.18 z 2 0.2 z 3 H ( z ) 0.1 0.6 1 0.4 z 1 0.5 0.2 z 1 1 0.8 z 1 0.5 z 2