Displaced ping imaging autofocus for a multi-hydrophone SAS

The problems caused by random sway errors in the tow-path of a single hydrophone, synthetic aperture sonar (SAS) are well known. If these horizontal displacement errors are left uncorrected and the tow-path is assumed to be straight, they have a devastating effect on the quality of the reconstructed image. Although on-board navigation instruments can help measure the gross departures from the straight path, sub-wavelength residual sway errors still corrupt the imaging process. More recent SAS systems have an array of receiving hydrophones and are effected by both random sway and yaw errors and again navigation instruments cannot always estimate the yaw and sway to the accuracy required to remove the effects. Current autofocus (sometimes called ´micronavigation´) techniques to estimate the sway and yaw from the raw (i.e. distorted) data rely on there being significant overlap of the hydrophone array between pings and so the SAS travels at much less than its maximum allowable speed (as determined by spatial sampling considerations). The requirement for significant overlap compromises the maximum achievable mapping rate. The authors show how the distorted raw data from a multi-hydrophone SAS can be used to estimate the sway and yaw even when the sonar is travelling at the maximum allowable speed. The mathematical framework is outlined using the wavenumber algorithm, modified for a multiple hydrophone-receiver SAS, as the final method of image reconstruction. The nub of the proposed autofocusing algorithm is the formation of individual images from each separate ping. These single-ping images from adjacent pings are then cross-correlated to determine how the sonar has moved between pings. Thus the differential yaw and differential sway can be estimated from a series of displaced single ping images and this process forms the basis of displaced ping imaging autofocus (DPIA). Using simulated, distorted SAS data as input, the proposed DPIA algorithm shows promise in that it can estimate yaw with sufficient accuracy to restore distorted SAS images close to their diffraction limit.