Real-time cognitive sonar system with target-optimized adaptive signal processing through multi-layer data fusion

In this contribution a proof-of-concept of a real-time cognitive sonar system is shown and tested in simulations. In the field of radar applications the principle of a cognitive system has been intensively studied over the past years. However, cognitive sonar systems draw only little attention in the reseach community. In this paper we are showing a new approach for a cognitive sonar system, that is able to adapt its signal processing to measured parameters of particular targets. This is possible by the fusion of the information of different data layers. Data resulting from an internal tracking module is used to adapt the signal processing at sensor layer. This is done by adjusting the transmit signal and the direction of the transmitted energy via transmit beamforming. These adjustments improve, on the one hand, the signal-to-noise ratio (SNR) and the signal-to-reverberation ratio (SRR) of the target through the adaption of the transmitted signal, and, on the other hand, extend the spatial awareness by adapting the transmit beamformer, which results in a combination of two spatial filters: the receive beampattern and the transmit beampattern.