The formation of high-resolution synthetic aperture sonar (SAS) imagery requires accurate estimates of the sensor's trajectory. This is frequently accomplished using the Displaced Phase Center Antenna technique, which utilizes cross correlation of the signals received on successive pings. Accurate estimates of the sensor's ping-to-ping advance are then made by measuring the along-track spatial coherence of the scattered field. Unbiased advance-per-ping estimates require an accurate model for the spatial coherence of the scattered field. This model may be found by the application of the van Cittert-Zernike theorem to the problem of pulsed active sonar systems. In the present work, it is shown that the spatial coherence for a typical highfrequency SAS collection geometry is well approximated by a Gaussian whose width is proportional to the sensor's element size. Gaussian and quadratic along-track interpolation kernel performance is compared for a pair of at sea data collections. A relative image quality metric, based on image contrast, is defined to quantitatively assess the performance of the pair of interpolation kernels. In both tests, the use of an along track estimator is shown to provide improved image quality. Also in both tests, the performance of the Gaussian kernel exceeds the quadratic kernel.

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A Correlation Velocity Log (CVL) has some advantages (lower source level and operating frequency) over a Doppler Velocity Log (DVL) as a navigational aid for an unmanned underwater vehicle (UUV). A CVL provides a bottom referenced velocity estimate by estimating displacement using the incoherently scattered field from an acoustic projector and an array of hydrophones. A small low cost UUV generally operates in shallow water and has limited space and power available for a navigational aid, creating added constraints for the design of a CVL. The important design considerations (such as size, array geometry, operating frequency, and bandwidth) will be discussed as they relate to accuracy of the velocity estimate and operating range. [The authors acknowledge the financial support for this work by Lockheed Martin Undersea Systems.]

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Synthetic aperture sonar imagery is typically generated using data collected with unmanned underwater vehicles. The prohibitive cost of collecting underwater data and the need for well-controlled factors such as collection geometry and object configuration has provided the motivation for devising a benchtop in-air circular acoustic data collection framework. This set-up makes it practically feasible to explore a multitude of parameters that are not as feasible with underwater measurement scenarios, including waveform type, object shapes and material. It is also practically feasible to explore various representations of the collected acoustic data that help better emphasize different aspects of the information embedded in the acoustic signal, which various machine learning algorithms can utilize. Signal processing and feature organization are critical to improving performance of machine learning algorithms. For example, geometric scattering response of objects is well-represented in spatial imagery with sharp contrast of pixel intensity between the object and surrounding environment, while spatial spectrum of the complex SAS image better represents the aspect-dependent spectral response of the object that help discriminate objects of the same shape, but with different material. We will discuss the relationship between the choice of representation and discriminatory information with illustrative classification problems.

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Sonar systems that exploit correlation for navigation, such as correlation velocity logs and micronavigation for synthetic aperture sonar, often make redundant estimates of the spatial coherence of the scattered field at several spatial lags. Two models for the correlation of these redundant measurements are described. First, an analytical model is derived using the assumption of stationary Gaussian statistics. Next, a numerical model is described that accounts for non-stationary processes present in measurements of seafloor scattering. These models are compared to normal-incidence scattering data collected at Seneca Lake, NY. Both models show good agreement with the measurements when the spatial separation between redundant hydrophone pairs is less than the coherence length. At greater spatial separation, the analytical model diverges from the measurements. This disagreement is explained by a lack of stationarity in the measured data which is captured by the numerical model. Finally, spatial variations in the volume scattering strength of the sediment are identified as a source of the non-stationarity in the measurements.