1. Adaptive Beamforming for Target Tracking in Cognitive MIMO Sonar
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Adaptive Beamforming for Target Tracking in Cognitive MIMO Sonar
Joseph Tabrikian
Ben-Gurion University of the Negev
Underwater Acoustics Symposium
June 19, 2014
Research student: Nathan Shraga

2. Outline Introduction MIMO radar/sonar data model
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Outline
Introduction
MIMO radar/sonar data model
Cognitive MIMO radar/sonar configuration
Adaptive beamforming for target localization
Adaptive beamforming for MIMO sonar – dynamic scenario
Simulations
Conclusions and future work

3. BGU
MIMO Data Model
vector of transmitted signals
target Tx Rx

4. BGU
MIMO Data Model
Discrete MIMO data model at the kth pulse for dynamic scenario with narrowband signals:
In matrix notation:

5. MIMO Sonar Model – Shallow Water Environment
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MIMO Sonar Model – Shallow Water Environment

6. MIMO Data Model Sufficient statistic model for AWGN:
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MIMO Data Model
Sufficient statistic model for AWGN:
Classical MIMO configuration with orthogonal signals:
Advantages: virtual sensors, array aperture extension, resolution enhancement, low probability of intercept (LPI), lower sidelobes, etc (Bekkerman-Tabrikian 2004).
Disadvantage: omni-directional transmission resulting in gain loss, which is inefficient in tracking.

7. Cognitive Radar/Sonar Configuration
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Cognitive Radar/Sonar Configuration
Optimal Adaptive Waveform Design
Optimal Receiver/Processor
Detection/
Estimation/
Tracking
noise
Target dynamics model
Optimal processor: Detect/localize/track the target based on the measurements,
Optimal adaptive waveform design: Design the transmit signal at the kth pulse, , based on the measurements during the previous pulses, to optimize a given criterion.

8. Optimization Criteria
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Optimization Criteria

9. Adaptive Beamforming for Static Target Localization
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Adaptive Beamforming for Static Target Localization
Criterion: Bayesian Cramér-Rao bound:
Convex optimization problem for both constraints:

10. Adaptive Beamforming for Target Tracking in Shallow Water
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Adaptive Beamforming for Target Tracking in Shallow Water
Target/channel dynamics:

11. Cognitive Beamforming and Tracking Configuration
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Cognitive Beamforming and Tracking Configuration
Target dynamic model
Prediction
Beamform optimization
Posterior computation
Parameter Estimation
noise
History

12. Simulations – Target Tracking in Uncertain Shallow Water
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Simulations – Target Tracking in Uncertain Shallow Water

13. BGU
Simulations

14. BGU
Simulations

15. Conclusions and Future Work
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Conclusions and Future Work
A new optimal waveform design approach for cognitive MIMO radar/sonar is proposed based on minimizing the BCRB at each step using the measurements from previous steps.
This approach provides an automatic focusing array: beamforming before detection.
The method was adapted to consider dynamic targets, which can be interpreted as track-before-detect in transmission.
The method was adapted to consider environmental uncertainties.
Further research will cover the following issues:
Wideband signal model with range and Doppler information (in progress).
Considering other optimization criteria, such as probability of detection or probability of resolution (in progress).
Realistic shallow water channel simulations

16. BGU
Thank you!

17. Cognitive Radar/Sonar
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Cognitive Radar/Sonar
In radar/sonar: A cognitive radar/sonar employs adaptive receiver and transmitter, based on measurements in the history as well as side information regarding the environment.
Ingredients of cognitive radars/sonars:
Intelligent signal processing based on the information obtained from interaction of the system with the environment.
Feedback from the receiver to the transmitter.
Preservation of the information content of radar returns.
The cognitive radar idea was first proposed by S. Haykin 2006.