In this paper, a new algorithm is introduced for high-precision underwater navigation using the coherent echo signals collected during repeat-pass synthetic aperture sonar (SAS) surveys. The algorithm is a generalization of redundant phase center (RPC) micronavigation, expanded to RPCs formed between overlapping pings in repeated passes. For each set of overlapping ping pairs (two intrapass and three interpass), five different RPC arrays can be formed to provide estimates of the vehicle's surge, sway, and yaw. These estimates are used to find a weighted least squares solution for the trajectories of the repeated passes. The algorithm can estimate the relative trajectories to subwavelength precision (on order of millimeters to hundreds of micrometers at typical SAS operating frequencies of hundreds of kilohertz) in a common coordinate frame. This will lead to improved focusing and coregistration for repeat-pass SAS interferometry and is an important step toward repeat-pass bathymetric mapping. The repeat-pass RPC micronavigation algorithm is demonstrated using data collected by the 300-kHz SAS of the NATO Center for Maritime Research and Experimentation (CMRE) Minehunting Unmanned underwater vehicle for Shallow water Covert Littoral Expeditions (MUSCLE).

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Because the use of multibeam echo-sounder imagery in sea-floor identification is constantly increasing, a semi-automatic mosaic interpreter is presented. It is based on the statistical and acoustical properties of the image pixels, and relies on the use of Markov Random Fields image models within a Bayesian framework for partitioning mosaics into homogeneous regions. Further, we introduce a Gibbs distribution model of the original image for computing its Maximum a Posteriori estimate. Effects of backscattering angular variations are compensated by injecting a first estimate of these into the calculation. Segmentation result of low-frequency multibeam mosaic is presented and compared to geological interpretation.

Accurate time delay estimation between signals is crucial for coherent imaging systems such as Synthetic Aperture Sonar (SAS) and Synthetic Aperture Radar (SAR). In such systems, time delay estimates resulting from the crosscorrelation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery.In this paper, an algorithm is introduced for detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit one-and two-dimensional models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase centre (RPC) micro-navigation. Phase wrap errors are then corrected by re-calculating the phase wrap number using the best-fitting model.The approach is demonstrated using data collected by the 270 -330 kHz SAS of the NATO Centre for Maritime Research and Experimentation (CMRE) Minehunting Unmanned underwater vehicle for Shallow water Covert LittoralExpeditions (MUSCLE). Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using one-and two-dimensional models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1D and 2D RANSAC methods out-perform the branch-cuts method, with improvements of 29 -125% and 30 -150% respectively compared to 16 -134% for the branch-cuts method for this dataset.