investigations into beam steering algoritithms for adaptive antenna arrays by siyandiswa juanitta...
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INVESTIGATIONS INTO BEAM STEERING ALGORITITHMS FOR ADAPTIVE ANTENNA ARRAYS
BY
Siyandiswa Juanitta Bangani
Supervisor: Dr R.Van Zyl
Cape Peninsula University of Technology
MTECH/MSC F’satie
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OUTLINE
• Objective• Approach• Algorithms under investigation
Classical (Conventional) Beamformer Capon’s Beam former MUSIC Algorithm Least Mean Square (LMS)
• Simulations• Discussions of results• Conclusions• Questions
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OBJECTIVE
• The main objective of the research is on directing the beam towards the desired target in a particular direction while successfully rejecting all other targets in unwanted directions
• The research is based on direction of arrival estimation and adaptive beamforming
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Objective Cont.
Desired Target direction
Interference direction
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Approach
• Two aspects to the research
Receive:
Direction of arrival algorithms (DOA) to locate targets Transmit:
Mechanism to steer main beam in required direction Linear phase shifting Weighted phase shifting
Slide 6 © CSIR 2006 www.csir.co.za
Receive: Beamformer- DOA
Beamforming Block diagram measurement and storage
of element signals
multiplication by weighting factors and
addition for all directions
calculation of output power yy*
searching for maximum outputpower as a function of direction
direction for maximum power = bearing
weighting factors forall directions
u
y=wHu
w*
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Classisal and Capone’s Beamformer
• In the classical beamforming approach for DOA estimation, the beam is scanned over the angular region of interest in discrete steps by forming weights w=a(Ø) for different Ø and the output power is measured
• The technique uses some of the degrees of freedom to form a beam in the desired look direction, while simultaneously using the remaining degrees of freedom to form nulls in the direction of interfering signals. Capon's method requires the computation of a matrix inverse, which can be computationally expensive for large antenna arrays.
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MUSIC Algorithm
• MUSIC algorithm is a high resolution MUltiple SIgnal Classification technique based on exploiting the eigenstructure of the input covariance matrix.
• Provides information about the number of incident signals, DOA of each signal, strengths and cross correlations between incident signals, noise power, etc.
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Beamforming-DOA Cont.
• Classical beamformer / Conventional beamformer
• Capon’s Beamformer
• MUSIC Algorithm
)()()( aRawRw uuH
uuHP
)()(
1)(
1
aRa uu
HP
)()(
1)(
aVVa Hnn
HP
Slide 10 © CSIR 2006 www.csir.co.za
DOA simulations
• Effect of SNR• Proximity
-200 -150 -100 -50 0 50 100 150 200-20
0
20
40Classical beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 200-40
-20
0
20Capon beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 2000
50
100MUSIC pseudo-spectrum
Angle (degrees)
dB
SNR =10 θ = [-750, 00, 750]
-200 -150 -100 -50 0 50 100 150 200-50
0
50Classical beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 200-100
-50
0
50Capon beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 2000
50
100MUSIC pseudo-spectrum
Angle (degrees)
dB
SNR =20 θ = [-750, 00, 750]
-200 -150 -100 -50 0 50 100 150 200-20
0
20
40Classical beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 200-40
-20
0
20Capon beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 2000
50
100MUSIC pseudo-spectrum
Angle (degrees)
dB
SNR =10 θ = [440,510]
-200 -150 -100 -50 0 50 100 150 200-50
0
50Classical beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 200-100
-50
0
50Capon beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 2000
50
100MUSIC pseudo-spectrum
Angle (degrees)
dB
SNR=20 θ = (440 and 510)
-200 -150 -100 -50 0 50 100 150 200-50
0
50Classical beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 200-100
-50
0
50Capon beamformer pseudo-spectrum
dB
-200 -150 -100 -50 0 50 100 150 2000
50
100MUSIC pseudo-spectrum
Angle (degrees)
dB
SNR= 20 θ = (460 and 490)
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TRANSMIT: Steering Mechanism
• Electronically steering by adapting the phases• Generating look up Table in FEKO
Linear phases
Weighted phases
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Linear phases -generated for Direction determination
Phi
(Ø) Theta
(θ)
Gain
(dB)
3dB BW
(degrees) Main side lobe
suppression
700 650 11.70 140 -12dB
800 650 11.78 170 -13dB
900 600 11.74 130 -12dB
1000 500 10.50 16 0 -5dB
1400 400 10.43 200 -12.5dB
150 0 300 10.15 300 -14dB
160 0 250 9.97 600 -3dB
1700 200 9.84 620 -1.5dB
1800 00 9.91 600 0dB
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Linear phase shifting and Weighted phase Shifting1000 linear phase shift
3D RADIATION GAIN PATTERN
1000 weighted phase shift
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Cont.
Cartesian plot
1000 linear phase shift 1000 weighted phase shift
-5dB -13dB
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Discussion of Results
• MUSIC algorithm gives better results as compared to the Classical and Capons beamformer
• The LMS algorithm improves the short comings of linear phase shift especially when it comes to radiation characteristics
• In improving the linear phase shift there is a trade off between desired direction and main side lobe level
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CONCLUSIONS
• In conclusion with the various simulation performed the algorithms investigated show possible results to realise the objective
• The combination of MUSIC algorithm for identifying DOA and LMS adaptive beamformer gives positive results
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Thank you