1 / 60

Analysis for Adaptive DOA Estimation with Robust Beamforming in Smart Antenna System

Analysis for Adaptive DOA Estimation with Robust Beamforming in Smart Antenna System. 指導教授：黃文傑 W.J. Huang 研究生　：蔡漢成 H.C. Tsai. Outline. Conception of Smart Antenna Beamforming Method DOA Estimation θ - LMS Algorithm (My Point) Local Minimum problems

emmly
Download Presentation

Analysis for Adaptive DOA Estimation with Robust Beamforming in Smart Antenna System

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Analysis for Adaptive DOA Estimation with Robust Beamforming in Smart Antenna System 指導教授：黃文傑 W.J. Huang 研究生　：蔡漢成 H.C. Tsai

  2. Outline • Conception of Smart Antenna • Beamforming Method • DOA Estimation • θ- LMS Algorithm (My Point) • Local Minimum problems • θ- LMS Algorithm with Robust Beamforming • Conclusion

  3. Conception of Smart Antenna • There are constructed by some specially geometric antenna array. • It changes the beam-pattern with some different methods. • It increases the CINR and Capacity

  4. Category -1 • Switched Beam System

  5. Category -2 Adaptive Beam System

  6. Outline • Conception of Smart Antenna • Beamforming Method • DOA Estimation • θ- LMS Algorithm • Local Minimum problems • θ- LMS Algorithm with Robust Beamforming • Conclusion

  7. DOA Estimation Beamforming Weighting Summation Beamforming Method

  8. Beamforming Tech. • Conventional Beam-former • Capon’s Beam-former Array Response y(t) Output power P(w)

  9. Y θ (M-1)d X d d Conv. Beamforming & Steering Vector

  10. ULA d = 0.5 λ M = 4、 8、 12

  11. MVDR Beamforming(Capon’s)

  12. Outline • Conception of Smart Antenna • Beamforming Method • DOA Estimation • θ- LMS Algorithm • Local Minimum problems • θ- LMS Algorithm with Robust Beamforming • Conclusion

  13. DOA Estimation • Conventional • Capon’s • Subspace • MUSIC

  14. DOAEst. u(n)Receiving signal Pattern(0~180) w(0~180)Conventional Conventional DOA Estimation w(n) BeamformingWeighting

  15. DOAEst. u(n)Receiving signal Pattern(0~180) w(0~180)Capon’s Capon’s DOA Estimation w(n) BeamformingWeighting

  16. MUSIC(MUltiple SIgnal Classification ) w(n)Weighting Vector u(n)Receiving signal DOAEst. Eigen decompositionNoise SpaceVn PMUSIC Pattern a(0~180)

  17. Compare the three methods ULA M = 4 d = 0.5λ User’s DOA = 90°、120 ° SNR=10dB

  18. Outline • Conception of Smart Antenna • Beamforming Method • DOA Estimation • θ- LMS Algorithm (My point) • Local Minimum problems • θ- LMS Algorithm with Robust Beamforming • Conclusion

  19. LMS (Least Mean Square )

  20. d(n) + u(n) e(n) w(n) y(n) w(n+1) w - LMS w – LMS Algorithm

  21. d(n) 4 x1 e(n) u(n) w(n) w(n+1) y(n) ө(n) ө(n+1) ө- LMS θ- LMS Algorithm +

  22. θ- LMS Algorithm u(n) w(n) wH(n) u(n) -d(n) Cost functionJ(θ) w(n)Weighting Beamforming Weighting by DOA θ0 find DOA θ0

  23. Weighting is Instead of θ

  24. Definition = • Adaptive θ(n) is defined

  25. ULA M = 4 d = 0.5λ DOA= 120 ° Initial DOA = 90 ° Step size = 0.01 SNR =20

  26. ULA M = 4 d = 0.5λ DOA= 0 °~180 ° Initial DOA = 90 ° Step size = 0.01 SNR =20 DOA=90*sin(0:0.01:80*pi) + 90;

  27. Steering Vector Tracking ULA M = 4 d = 0.5λ DOA= 0 °~180 ° Initial DOA = 90 ° Step size = 0.01 “*” steering vector “o” tracking vector DOA=90*sin(0:0.05:80*pi) + 90;

  28. Beampattern Tracking ULA M = 4 d = 0.5λ DOA= 0 °~360 ° Initial DOA = 90 ° Step size = 0.01 “o” DOA DOA=180*sin(0:0.05:80*pi) + 180;

  29. Outline • Conception of Smart Antenna • Beamforming Method • DOA Estimation • θ- LMS Algorithm • Local Minimum problems • θ- LMS Algorithm with Robust Beamforming • Conclusion

  30. Converse to Error Direction ULA M = 4 d = 0.5λ DOA= 90 ° Initial DOA = 120 ° Step size = 0.01

  31. Error pattern d(n d(n ) ) + 4 x1 4 x1 e(0~180) x x (n (n ) ) w(0~180)

  32. Cost function ULA M = 4 d = 0.5λ DOA= 90 °

  33. DOA=90 ° Error Surface (DOA ) ULA M = 4 d = 0.5λ DOA= 0 °~180 °

  34. d=0.5 λ Error Surface (d ) ULA M = 4 d = 0~1λ DOA= 90 °

  35. M=4 Error Surface (M ) ULA M = 2 ~8 d = 0.5λ DOA= 90 °

  36. d(n) 2 x1 e(n) u(n) + w(n) w(n+1) y(n) ө(n) ө(n+1) ө- LMS 2 Antsθ- LMS Algorithm

  37. Error Surface (M=2 d=0.5 ) ULA M = 2 d = 0.5λ DOA= 90 °

  38. Error Surface (M=2 d=0.25 ) ULA M = 2 d = 0.25λ DOA= 90 °

  39. Simulation (1) ULA M = 2 d = 0.25λ DOA= 5 ° Initial DOA = 175 ° SNR = 30 dB

  40. Simulation (2) ULA M = 2 d = 0.25λ DOA= 170 ° Initial DOA = 5 ° SNR = 30dB

  41. 2 – 4 “θ-LMS” 2 antennas “θ-LMS” 4 antennas “θ-LMS” Initial θ

  42. Outline • Conception of Smart Antenna • Beamforming Method • DOA Estimation • θ- LMS Algorithm • Local Minimum problems • θ- LMS Algorithm with Robust Beamforming • Conclusion

  43. Noise problem • θ- LMS Algorithm needs high SNR level. • High noise level brings the DOA estimation result worse. • The DOA estimation error will cause the terrible performance • We use the Robust Beamforming Method to conquer the estimation error problem.

  44. The flow chart of Robust Beamforming Ref : Riba J., Goldberg J., Vazquez G., “Robust Beamforming for Interference Rejection in Mobile Communications”, Signal Processing, IEEE Transactions on

  45. Robust Beamforming

  46. BER Analysis BPSK ULA M = 4 d = 0.5 λ DOA = 90°

  47. Outline • Conception of Smart Antenna • Beamforming Method • DOA Estimation • θ- LMS Algorithm • Local Minimum problems • θ- LMS Algorithm with Robust Beamforming • Conclusion

  48. Conclusion • DOA is an important parameter for beamforming system. • But, the MUSIC algorithm is complex. • The new method “ө - LMS” is simpler to realized • Robust Beamforming can repair the fault of “ө - LMS”

  49. Future Work • Noise and channel problem • Multi-user problems • SINR Analysis • Multi-path & DOA distribution • Moving Source analysis • Performance Analysis(User # 、 DOA、SNR 、 Beamforming method、Antenna # …etc.) • Adaptive Analysis(Step-size Moving DOA、SNR 、other adaptive structure)

  50. Capon’s Beamforming

More Related