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Lisha Li, Brian D. Jeffs, Andrew Poulsen, and Karl Warnick Brigham Young University

Analysis of Adaptive Array Algorithm Performance for Satellite Interference Cancellation in Radio Astronomy. Lisha Li, Brian D. Jeffs, Andrew Poulsen, and Karl Warnick Brigham Young University XXVII URSI General Assembly 2002. Summary.

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Lisha Li, Brian D. Jeffs, Andrew Poulsen, and Karl Warnick Brigham Young University

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  1. Analysis of Adaptive Array Algorithm Performance for Satellite Interference Cancellation in Radio Astronomy Lisha Li, Brian D. Jeffs, Andrew Poulsen, and Karl Warnick Brigham Young University XXVII URSI General Assembly 2002

  2. Summary • GLONASS, Iridium, ground-based radars, etc. create overwhelming interference in important bands. • Adaptive beamforming/array processing algorithms are promising for interference cancellation, but are not well characterized in radio astronomy environment. • Low SNR, very sparse arrays, very high gain elements. • We study five algorithms for a small telescope array. • A real-time MSC, LMS filter is implemented.

  3. The Algorithms LCMV: Linear constraint minimum variance GSC: Generalized sidelobe canceller MSC: Multiple sidelobe canceller MSNR: Max. signal to noise ratio SPSN: Subspace projection spatial nulling

  4. Algorithm Applicability • Adaptive beamforming: LCMV, GSC, MSC, MSNR: • For more compact arrays or sub-arrays. • Single channel output – array performs as single high gain telescope (like GBT). • Candidate for SKA sub-arrays. • Array nulling: SPSN, modified MSC: • For large imaging arrays. • Output is full array, usable with synthesis correlator.

  5. Calibrated Array SINR Improvement Comparisons • SINR at feeds is -60 dB, SNR = -14 dB. • Average interference reduction of 45 dB. • Source is at zenith. • 8 MHz processing bandwidth. • MSC is least affected by grating lobes. • Grating lobes are a big problem for small array. - - - - - - - -

  6. - - - - - - - - SINR Performance with Calibration Errors • Circular complex Gaussian calibration error, mean = 1, variance = 0.01 • Relative performance among algorithms similar to calibrated case. • Significant SINR improvements still, but larger variation.

  7. LCMV Null Placement and Mainlobe Distortion • LCMV for 3 element VSA, tight 15 ft. spacing. • Interferer is in 3m dish mainlobe, and array grating lobe. • Null placement and cancellation are good. • Array mainlobe is distorted.

  8. A Real-time MSC, LMS Filter Adaptive Cancellation Experiment Interference Cancelled Signal Primary Antenna Periodogram PSD Estimator d[n] e[n] + Ss(wk) + - x[n] y[n] h[n] Reference (Auxiliary) antenna m Periodogram PSD Estimator Si(wk)

  9. VSA Antenna Array in Adaptive Canceling Experiment • Reference antenna aimed at interference / photographer. • Right dish is primary channel, aimed at Cassiopeia.

  10. Real-time LMS Filter Parameters • 13 full complex adaptive FIR filter taps. • 500 kHz processing bandwidth, I-Q baseband. • Complex LMS filter update algorithm. • VSA 3m dish antennas used for signal and reference. • Primary antenna steered to Cassiopeia. • Reference antenna steered to roof mounted interference source.

  11. Real-time LMS Filter Parameters (cont.) • Interferer is F.M. sweep modulation, • 100 kHz BW • Carrier centered at 1420.66 MHz • -62 dBm at dipole 95 ft. from receiver dish. • 1024 bin (500 Hz per bin) periodogram spectral estimate computed in real-time. • Integrate and download every 2s.

  12. Advantages of Real-time Cancellation • Operates on raw pre-correlator sampled data. • Can be inserted as a transparent front-end process in an existing telescope system. • No long-duration, high data rate recording needed. • Useful if time-sample outputs are desired, not integrations. • Post processing adaptive filtering requires huge data storage. • DSP hardware (programmable and FPGA) are now fast enough to support desired bandwidths.

  13. VSA Test Platform Receiver • Analog receiver (foreground): • 4 channels • 16 MHz bandwidth • 59 K total system noise • DSP array processor in (background): • 4 channels, 65 MHz A/D. • Digital Receiver front-end. • 4 TMS320C6201 floating-point processors.

  14. DSP Detail • 4-200 MHz processors with digital receiver front-ends. • In real-time, performs: • Complex baseband, band select, decimate, filter. • Two channel 1024 point periodogram and accumulate. • 13 complex tap FIR LMS adaptive filter. (four multiplies per tap).

  15. Hydrogen Line and Interference Signals FM interference signal seen by reference antenna. Real-time PSD Estimate Hydrogen signal from Cassiopeia seen by primary antenna. 28 minute PSD integration. Automatic tracking.

  16. Real-time Cancellation Results Signal and Interference Seen by the primary antenna. LMS MSC adaptive canceller output. Real-time result. 30 min. integration PSD.

  17. Noise Floor Calibration Signal(using RF absorber in feed)

  18. Conclusions • Adaptive beamforming algorithms are promising for compact arrays, sub-arrays, array feeds. • MSC is most robust, • No grating lobe problems. • Useful for both beamforming and imaging arrays. • The real-time LMS MSC was very successful, should work in many environments. • Next step: test real-time MSC with GBT.

  19. Hydrogen Line Signal, Cassiopeia

  20. FM Interferenceat Reference Receiver

  21. Signal plus Interference at Primary Receiver

  22. Adaptive Canceller Output

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