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Blind Beamforming for Cyclostationary Signals

Blind Beamforming for Cyclostationary Signals. Array Processing Project Preeti Nagvanshi Aditya Jagannatham. Conventional Beamforming. Based on DOA estimation Intensive Computation, Calibration Based on known training signal Synchronization, Sacrifice of bandwidth. Blind Beamforming.

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Blind Beamforming for Cyclostationary Signals

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  1. Blind Beamforming for Cyclostationary Signals Array Processing Project Preeti Nagvanshi Aditya Jagannatham

  2. Conventional Beamforming • Based on DOA estimation • Intensive Computation, Calibration • Based on known training signal • Synchronization, Sacrifice of bandwidth Blind Beamforming • No reference signal required • No advance knowledge of the correlation properties • No Calibration is necessary • Selectivity is achieved using knowledge of cycle frequency

  3. Cyclostationary Statistics • b(K) is random, s(t) does not contain first order periodicities • b2(t) = 1 (BPSK), s2(t) is periodic • Spectral Lines at  = (±2fc ± mf b)

  4. Data Model: Data Model: • sk(n), k= 1,…….,K K narrowband signals from DOA k • i(n) Interferers, v(n) white noise • x(n) is Mx1 complex vector, M = array size

  5. Cyclic Correlation: • - time average over infinite observation period • no is some time shift,  is the cycle frequency Cyclic Conjugate Correlation:

  6. Cyclic Adaptive Beamforming(CAB): • wCAB is a consistent estimate of d() Multiple desired signals (same )...

  7. Constrained Cyclic Adaptive Beamforming(C-CAB): • True DOA of the desired signal is unknown, wCAB  d() • C-CAB  MPDR with d() replaced by wCAB Robust Cyclic Adaptive Beamforming(R-CAB):

  8. Fast Adaptive Implementation: • Rxu(N) is updated every sample • Use matrix inversion lemma to compute the inverse • Complexity wCAB(N) is O(M), wCCAB(N) is O(M2) compared to O(M3)

  9. Simulation: Experiment1-Carrier Recovery • 2 BPSK signals • 100% cosine rolloff • Data rate - 5Kbps • Carrier - 5MHz • Carrier offset - 0.00314 • s =40º, I =120º •  = 0 • M = 4 (array size)

  10. Simulation (contd.): Experiment2-Moving source DOA estimation • Sampling - 150K samples/s • s =40º - 130º • SNR = 8 dB • SNRI = 4 dB • M = 16 (array size) • Updated every 0.1s • Uses 60 symbols(300 Samp) • Interferer at 30º

  11. Simulation (contd.): Experiment2-Moving source DOA estimation (contd.)

  12. Simulation (contd.): Experiment3-Multipath signals • s1 =30º, s2 =40º, I =120º • SNR1 = 15 dB • SNR2 = 12 dB • SNRI = 1 dB • M = 10 (array size)

  13. Simulation (contd.): Experiment4-Multiple signals • s1 =130º, s2 =60º, I =10º • SNR1 = 15 dB • SNR2 = 9 dB • SNRI = 1 dB • M = 15 (array size)

  14. Conclusions… • Achieved blind beamforming exploiting the cyclostationarity property of the communication signal • Using structure of the signals better signal processing techniques can be developed References… “Blind Adaptive Beamforming for Cyclostationary Signals”- Trans. SP, 1996 “Statistical spectral analysis – A non probabilistic theory”- William A. Gardner

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