Repeat-pass synthetic aperture sonar micro-navigation using redundant phase center arrays

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)

Repeat-pass synthetic aperture sonar micro-navigation using redundant phase center arrays

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)

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

An operational concept for correcting navigation drift during sonar surveys of the seafloor

The accumulation of navigation errors (drift) is a problem in many applications of autonomous underwater vehicles (AUVs), particularly during long-duration underwater surveys. Traditional methods for correcting drift require either surfacing of the vehicle for a global navigation satellite systemupdate or use of an independent acoustic positioning system. These methods may not be desirable or possible due to mission constraints. We propose a solution to this problem completely underwater and without the aid of external navigation systems. The approach is based on an operational concept that uses a modified paired-track survey pattern combined with through-the-sensor navigation corrections from a seafloor imaging sonar. We describe the operational concept, derive a model for its performance limits, validate this model, and demonstrate the concept with real experiments at sea. Using this approach, we provide an opportunity to use either coherent or incoherent through-the-sensor positioning corrections for a mission length increase of only the product of the intratrack spacing and the number of track pairs. We show results from a proof-of-principle experiment using data collected by the 300-kHz synthetic aperture sonar of the NATO Centre for Maritime Research and Experimentation’s Minehunting Unmanned underwater vehicle for Shallow water Covert Littoral Expeditions

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Signal Processing for Synthetic Aperture Sonar Image Enhancement

This thesis contains a description of SAS processing algorithms, offering improvements in Fourier-based reconstruction, motion-compensation, and autofocus. Fourier-based image reconstruction is reviewed and improvements shown as the result of improved system modelling. A number of new algorithms based on the wavenumber algorithm for correcting second order effects are proposed. In addition, a new framework for describing multiple-receiver reconstruction in terms of the bistatic geometry is presented and is a useful aid to understanding. Motion-compensation techniques for allowing Fourier-based reconstruction in widebeam geometries suffering large-motion errors are discussed. A motion-compensation algorithm exploiting multiple receiver geometries is suggested and shown to provide substantial improvement in image quality. New motion compensation techniques for yaw correction using the wavenumber algorithm are discussed. A common framework for describing phase estimation is presented and techniques from a number of fields are reviewed within this framework. In addition a new proof is provided outlining the relationship between eigenvector-based autofocus phase estimation kernels and the phase-closure techniques used astronomical imaging. Micronavigation techniques are reviewed and extensions to the shear average single-receiver micronavigation technique result in a 3 - 4 fold performance improvement when operating on high-contrast images. The stripmap phase gradient autofocus (SPGA) algorithm is developed and extends spotlight SAR PGA to the wide-beam, wide-band stripmap geometries common in SAS imaging. SPGA supersedes traditional PGA-based stripmap autofocus algorithms such as mPGA and PCA - the relationships between SPGA and these algorithms is discussed. SPGA's operation is verified on simulated and field-collected data where it provides significant image improvement. SPGA with phase-curvature based estimation is shown and found to perform poorly compared with phase-gradient techniques. The operation of SPGA on data collected from Sydney Harbour is shown with SPGA able to improve resolution to near the diffraction-limit. Additional analysis of practical stripmap autofocus operation in presence of undersampling and space-invariant blurring is presented with significant comment regarding the difficulties inherent in autofocusing field-collected data. Field-collected data from trials in Sydney Harbour is presented along with associated autofocus results from a number of algorithms