Depth estimation of water column objects using interferometric synthetic aperture sonar

Abstract

Synthetic Aperture Sonar (SAS) is a technique which delivers sonar images of the seabed with both high resolution and large area coverage rate. SAS is therefore a well suited sensor for search of small objects on the seafloor, and is an important tool in many emerging mine countermeasures (MCM) systems. An interferometric SAS system can also resolve the angle of arrival in the vertical plane, and thus estimate the depth of an object. In this talk, we present techniques using SAS imaging for depth estimation of small objects, not on the seafloor, but located in the water column. We consider the effect of geometry, sensor settings and processing parameters. For objects of interest, we present a belief propagation inspired method for estimating the depth of the objects. This method is CPU intensive, but avoids the phase ambiguity problem encountered in standard SAS interferometry. We compare this method to a coarse depth estimate acquired from the multipath response in the SAS images as well as to ground truth. We show example images and depth estimates from the Kongsberg HISAS interferometric SAS collected by a HUGIN autonomous underwater vehicle.

Adaptive phase estimation filter in long range synthetic aperture sonar interferometry

“…Combined with the fact that the curves have the same functional shape and that they differ by a relatively small factor, the performance of our depth estimator can be described by the CRLB, up to a small factor. This is also supported in previous studies [5, 15, 16].…”

“…The horizontal resolutions of the interferometric estimate, $$\delta x$$ and $$\delta y$$, are simply the size of the estimation filter. This is equal to the grid resolution, dξ, times the number of pixels on one edge of the filter, M $$$$\begin{equation} \delta x=\delta y=M\mathrm{d}\xi \end{equation}$$$$The theoretical lower bound for the standard deviation of the depth estimate, $$\sigma _z$$, when omitting wrap errors, is given by the square root of the Cramér–Rao lower bound (CRLB) of the time delay estimate [15, 16] $$$$\begin{equation} \sigma _z \approx \frac{\text{rc}}{D} \, \frac{1}{2\pi f_\text{c}} \, \frac{1}{\sqrt {N}} \, \sqrt {\frac{1}{\rho }+\frac{1}{2\rho ^2}} \end{equation}$$$$where r is slant range, c the speed of sound, D the vertical baseline, $$f_\text{c}$$ the center frequency and ρ the SNR. Note that this expression assumes vertical interferometer baseline.…”

“…Note that this expression assumes vertical interferometer baseline. The SNR, ρ, can be estimated from the correlation coefficient or the interferometric coherence, μ [16, 17] $$$$\begin{equation} \rho = \frac{\mu }{1-\mu } \end{equation}$$$$…”

Exploitation of SAS interferometric phase statistics for coherent change detection

“…Single pass pairs are simultaneously collected from two separate receiver arrays on the same platform, while repeat‐pass pairs are collected from a single receiver array but at different times. There are two common applications for interferometric processing: () bathymetric mapping of the seafloor [1, 2] which typically uses single‐pass pairs, and () coherent change detection (CCD) [3–5] using a repeat pass collection. CCD is a powerful method of detecting changes in a scene by making use of the coherent properties of SAS images and allows detection of sub‐resolution sized changes [5].…”

Long-Range Interferometric Synthetic Aperture Sonar

“…The imaging swath is typically limited to no more than a few hundred meters to each side. For deep-water bathymetric mapping, there usually is enough signal energy to achieve sufficient signal-to-noise ratio (SNR) also out to maximum range 2 . Due to sonar beampatterns and the geometry limited principle of interferometric processing, there is a blind zone called the nadir gap directly below the AUV and out to a maximum grazing angle, where the seabed cannot be mapped.…”