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DFT Filter Banks

DFT Filter Banks. Steven Liddell Prof. Justin Jonas. Channelization. A common task in radio astronomy is the channelization of a signal onto separate frequency channels. The output signal has a decreased bandwidth so the output sample rate can be decrease multirate systems.

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DFT Filter Banks

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  1. DFT Filter Banks Steven Liddell Prof. Justin Jonas

  2. Channelization • A common task in radio astronomy is the channelization of a signal onto separate frequency channels. • The output signal has a decreased bandwidth so the output sample rate can be decrease multirate systems.

  3. Why Channelise a signal? • Allow computation to be performed on a narrower bandwidth and in parallel. • Implement the F in an FX correlator. • RFI mitigation. • Spectrum analysis. • Pulsar dedispersion

  4. How to Channelize a Signal • Analogue filter banks. • Unstable; Would rather use digital signals. • Fast Fourier Transform. • Fast; Not a great frequency response. • Digital filter banks • More computation required; Get a good response. • Discrete Fourier Transform (DFT) filter banks.

  5. FFT vs Filterbanks • FFT has a higher processing loss => decreases the instruments sensitivity.

  6. Computational Costs ≈N/2 log2(M) MACs M × N MACs

  7. DFT Filter Banks • DFT filter banks arise by modifying the FFT’s windowing function to provide channels with improved stop band attenuation and a narrower transition width. • The modified window is based on a prototype filter which lends its frequency response to each channel. • Two architectures of DFT looked at.

  8. DFT Filter Banks Weighted Overlap Add Filter Bank Polyphase Filter Bank ≈Mlog2(M)+N MACs

  9. The Polyphase Filter Bank • Replace a FFT’s window with a set of polyphase filters. • Create polyphase filters from a prototype filter: Prototype filter Polyphase filters (pρ(n))

  10. Prototype filter copied onto each channel.

  11. Aliasing Critically sampled (output data rate 1/16 input data rate) Over Sampled (output data rate >1/16 input data rate)

  12. Wola Filter Bank • The Weighted Overlap and Add filter bank. • Mathematically identical to polyphase filter. • Implementation different decouple number of channels from sample rate change factor.

  13. WOLA Filter Bank • Weighted Overlap Add:

  14. Fixed point arithmetic leads to a errors in the system. • Quantization error can be modelled as noise injected at a multiplier. • Error occurs in both the FIR and FFT so need to balance the number of bits.

  15. Fixed point error in the filter coefficients change the channels’ frequency response.

  16. Efficient through use of FFT but with good frequency response. • Easily implemented in parallel hardware. • Inherent sample rate change. • Replacing the stand alone FFT in signal paths requiring high accuracy channelization.

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