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

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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

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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