Signal Processing Applications for Submarines, Surveillance and Survival

Monday December 11, 2000, Afternoon

Adaptive signal processing and neural network based methodologies are topics of active research within Australia’s Defence Science and Technology Organisation.

Their application to sonar systems and land-based acoustic surveillance systems is considered here. For example, submarines have both hull-mounted and towed arrays of hydrophonic sensors. The digital data from these sensors are processed to extract information on acoustic signals propagating in the underwater environment.

Basic signal processing for passive sonar arrays involves applying the fast Fourier transform in both the temporal and spatial domains. More advanced sonar processors implement adaptive beamforming algorithms to maximise the output signal-to-noise ratio of the sonar array in spatially correlated noise fields. Even at frequencies well below the design frequency of the array, these optimal spatial filters are shown to enhance spatial resolution, to minimise sidelobes, and to steer nulls in the direction of interfering sources. However, adaptive beamforming can lead to signal suppression in the presence of system errors caused by the phase or amplitude responses of the sensors being mismatched, or by the knowledge of the sensor positions being imperfect.

During an ownship manoeuvre, this signal suppression property can be exploited to refine the position estimates of the acoustic sensors in a long flexible array towed behind the submarine. Passive sonar signal processing algorithms developed for submarine applications can be modified to process microphonic sensor data for acoustic surveillance of the land environment.

Unattended ground-based acoustic sensing systems are often deployed in remote locations to detect, classify, localise and track sources of military interest such as aircraft, ground vehicles and weapon fire.

Finally, the detection and classification of sea mines currently requires a purpose-built naval vessel to enter the minefield so that the capability and operational performance of the minehunter’s active sonar system are critical to mission success and survival. Various signal and image processing algorithms, some of which feature neural networks, are being developed for mine-hunting applications. These algorithms are described and the results presented.