Adaptive Beamforming for Target Tracking in Cognitive MIMO Sonar BGU 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

Outline Introduction MIMO radar/sonar data model BGU 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

BGU MIMO Data Model vector of transmitted signals target Tx Rx

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

MIMO Sonar Model – Shallow Water Environment BGU MIMO Sonar Model – Shallow Water Environment

MIMO Data Model Sufficient statistic model for AWGN: BGU 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.

Cognitive Radar/Sonar Configuration BGU 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.

Optimization Criteria BGU Optimization Criteria  

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

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

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

Simulations – Target Tracking in Uncertain Shallow Water BGU Simulations – Target Tracking in Uncertain Shallow Water

BGU Simulations

BGU Simulations

Conclusions and Future Work BGU 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

BGU Thank you!

Cognitive Radar/Sonar BGU 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.