Presentation on Adaptive Diagonal Loading for Robust Minimum Power Distortionless Response Beamformer

1. PRESENTATION
ON
SELECTED LITERATURE
EE 602 : DIGITAL SIGNAL PROCESSING
KAZI NEWAJ FAISAL
PHD STUDENT, REG: 21-52-21
ELECTRONICS ENGINEERING DEPARTMENT

2. DETAILS OF SELECTED LITERATURE
 Title: Adaptive Diagonal Loading for Robust
Minimum Power Distortionless Response
Beamformer.
 Authors: Sahebgowda S Patil, M Sivasankar,
Venkatesha K, A Vengadarajan
 Affiliations: Electronics & Radar Development
Establishment, DRDO, C. V. Raman
Nagar, Bangalore-560093
 Conference: 9th International Radar Symposium India
- 2013 (IRSI - 13)

3. BACKGROUND PRELIMINARIES
Beamforming
Ref: https://gfycat.com/niftydazzlingjay-beamforming-microwave-antenna-phased

4. BACKGROUND PRELIMINARIES
Beamforming
Ref: https://gfycat.com/tangibleweepyclumber

5. BACKGROUND PRELIMINARIES
Beamforming
Ref: https://gfycat.com/bowedimperfectboaconstrictor

6. BACKGROUND PRELIMINARIES
Ref:
https://commons.wikimedia.org/wiki/File:Phased_array_imaging_animation.gif
Beamforming
Ref: https://gfycat.com/tangibleweepyclumber

7. BACKGROUND PRELIMINARIES
Ref: DOI: 10.5772/intechopen.98702
Beamforming
Analog Beamforming Digital Beamforming

8. BACKGROUND PRELIMINARIES
Ref: DOI: 10.5772/intechopen.98702
Beamforming
 The fundamental aim of beamforming is to estimate the
desired signal properties by adjusting the complex
weights at each sensor applied to the received signal
which result in enhancement of desired signal and
place nulls in the direction of interference.
 Adaptive arrays are capable to adjust its weights
automatically according to the environment.
 Adaptive antenna array takes the output SINR as an
index to compute the optimal weights by maximizing
the output SINR.

9. BACKGROUND PRELIMINARIES
Beamforming
 Conventional Beamforming: optimal in white noise, does not take
into account other signals (interference) present in some directions.
 Adaptive beamforming: takes into account these other signals.
minimizing the output power while maintaining a unit gain towards
looked direction, tends to place nulls towards interfering signals.
 Optimal beamformer: Maximize SINR while ensuring a unit gain
towards towards looked direction
 Minimum Variance Distortionless Response (MVDR): Minimize
output power using interference plus noise covariance matrix.
 Minimum Power Distortionless Response (MPDR): Minimize output
power using signal plus interference plus noise covariance matrix.

10. BACKGROUND PRELIMINARIES
Summary of Adaptive Beamformers (Known Covariance Matrices)

11. CONTRIBUTION OF THE LITERATURE
 To compute the adaptive weights, Recursive Least
Squares method and Steepest Decent method converge
much slower than that of Sample Matrix Inversion
method.
 Sample Matrix Inversion method needs to estimate the
sample covariance matrix from the available data.
 The data used to estimate the covariance matrix should
not contain the desired signal, but in practical
applications it is not possible to completely eliminate the
desired signal.

12. CONTRIBUTION OF THE LITERATURE
 If the data used to estimate the covariance matrix has the
desired signal, then the adaptive algorithm places null in
the direction of the signal of interest depending on the
signal to noise ratio.
 Diagonal loading is one of the most widely used and
effective method to improve robustness of adaptive
beamformer, but selecting the diagonal loading value has
become a key aspect
 In the absence of complete knowledge of signal
characteristics and continuously changing environment,
fixed diagonal loading method may not give desired
performance, hence the adaptive method is essential.

13. CONTRIBUTION OF THE LITERATURE
 The authors have proposed an adaptive diagonal loading
method for MDPR beamformer, which systematically
computes the diagonal loading value based on the
covariance matrix and its eigen values.
 The authors have also proposed to use External Signal
Indicator (ESI) to indicate the presence of external signal.
when ESI is enabled their proposed method is going to
be applied otherwise conventional beamformer is used
i.e. adaptive beamformer is performed only if the external
interference is present.

14. CONTRIBUTION OF THE LITERATURE
 The authors have analysed the covariance matrix with
Rratio, where
 When there is no external signal then value Rratio will be
very high. When there is a external signal Rratio will
converge towards unity depends on the external signal
power.
 The authors have calculated the adaptive diagonal
loading level from the eigen values and the non-diagonal
mean of the spatial covariance matrix.

15. FINDINGS OF THE LITERATURE

16. FINDINGS OF THE LITERATURE

17. FINDINGS OF THE LITERATURE

18. OUTCOMES OF THE LITERATURE
 The authors has discussed about the deficiency of
MPDR beamformer, fixed diagonal loading and proposed
an adaptive diagonal loading method for robust adaptive
nulling of interference and maintain the unit gain in the
signal direction.
 Simulation results has demonstrated that proposed
method is performing better than MPDR and fixed
diagonal loading under different scenarios.

19. OUTCOMES OF THE LITERATURE
 If SNR is much higher than INR, other methods puts
perfect null in the signal direction along with the jammer
direction, hence the signal is rejected and cannot be
detected.
 Whereas in the proposed method signal rejection is very
minimal and the null in the jammer direction is partial, this
is acceptable because the signal strength is sufficient
enough that it can be detected by the detection process.