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AERA filters of the RFI in cosmic rays radio detection

AERA filters of the RFI in cosmic rays radio detection. Zbigniew Szadkowski, University of Łódź, Poland ECRS , Barnaul , July 201 8. Filtering by the FFT technique. IIR filter currently in use. IIR filter in the field. Extremely efficient , but Non-adaptive. Line a r predictor.

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AERA filters of the RFI in cosmic rays radio detection

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  1. AERA filters of the RFI in cosmic rays radio detection Zbigniew Szadkowski, University of Łódź, Poland ECRS, Barnaul, July2018

  2. Filtering by the FFT technique ECRS, Barnaul, July 2018

  3. IIR filter currently in use ECRS, Barnaul, July 2018

  4. IIR filter in the field Extremely efficient, but Non-adaptive ECRS, Barnaul, July 2018

  5. Linear predictor ECRS, Barnaul, July 2018

  6. Math. background ECRS, Barnaul, July 2018

  7. FPGA data flow ECRS, Barnaul, July 2018

  8. Example of filtered signals - cntd. • 64 stages in the FIR filter, • Many narrow band RFI suppressed by the factor of 3 – 5. ECRS, Barnaul, July 2018

  9. Improved FIR ECRS, Barnaul, July 2018

  10. FPGA data flow Solving of linear equations – Levinson procedure Covariances ECRS, Barnaul, July 2018

  11. Levinson procedure ECRS, Barnaul, July 2018

  12. Hardware implementation ECRS, Barnaul, July 2018

  13. AdaptiveIIR-notch ECRS, Barnaul, July 2018

  14. 4 IIR filters in a cascade way ECRS, Barnaul, July 2018

  15. Normalized Least Mean Square • Eachiteration of LMS algorithm involves : • Error estimation: • Adaptation of coefficients: • Updating of learning factors: Division in the FPGA ECRS, Barnaul, July 2018

  16. NLMS ECRS, Barnaul, July 2018

  17. DLMS ECRS, Barnaul, July 2018

  18. DLMS – a single carrier For µ = 1/128, 1/256 or 1/512 the mono-carrier is suppressed to ZERO !!! ECRS, Barnaul, July 2018

  19. DLMS – 4mono-carriers ECRS, Barnaul, July 2018

  20. DLMS – 32- vs. 64 stages ECRS, Barnaul, July 2018

  21. DLMS32 – echoedsignals ECRS, Barnaul, July 2018

  22. ILMS32 ECRS, Barnaul, July 2018

  23. DLMS – measurements ECRS, Barnaul, July 2018

  24. DLMS – Front-End used for tests ECRS, Barnaul, July 2018

  25. DLMS – FM filtering ECRS, Barnaul, July 2018

  26. DLMS – 4 carriers ECRS, Barnaul, July 2018

  27. DLMS – exact and deviated ECRS, Barnaul, July 2018

  28. Filtered spectra of ILMS32 ECRS, Barnaul, July 2018

  29. ILMS32 for µ=1/256 , µ=1/1024 ECRS, Barnaul, July 2018

  30. IIR filter ECRS, Barnaul, July 2018

  31. Distortion factor ECRS, Barnaul, July 2018

  32. LMS stability • Thefirst condition, attributed to Butterweck1(1995),states that the convergence necessary condition can beexpressed as: • 0 < μ <2/λmax • where λmax is the largest eigenvalue of the autocorrelation matrixR • 1ButterweckH. (1995), A steady-state analysis of theLMS adaptive algorithm without use of the independence assumption, Proceedings of ICASSP, pp. 1404–1407 ECRS, Barnaul, July 2018

  33. Stability for 2014-2015 ECRS, Barnaul, July 2018

  34. Stability for 2016-2017 ECRS, Barnaul, July 2018

  35. Conclusions • The ILMS32is adaptive in contradiction to the IIR notch currently in use, • The ILMS32does not use a division operation as the NLMS. It guarantees faster registered performance and stability, • For learning factors µ = 1/32 – 1/256 the suppression of multi-carriers is efficient and very fast, • The converge time is at the level of several microseconds in contradiction to previous FIR filters based on the linear prediction: • With FIR coefficients calculated in NIOS (~600 ms), • In HPS (20 ms) • Directly in the FPGA fabric (as Levinson algorithm) (600 µs). • Laboratory measurements fully confirmed very high efficiency of the ILMS • This ILMS filter as very efficient and adaptive should be tested in the field to be an potential alternative for non-adaptive IIR filter. ECRS, Barnaul, July 2018

  36. ECRS, Barnaul, July 2018

  37. AdaptiveIIR-notch ECRS, Barnaul, July 2018

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