Effect of incorrect sound velocity on synthetic aperture sonar resolution

Synthetic aperture sonar (SAS) is an imaging technique to produce centimeter resolution over hundreds-of-meter range on the sea floor, by constructing a virtual aperture whose length automatically adjusts itself for a given focusing range. SAS is near-field acoustic imaging, and this implies that the sound velocity should be accurately estimated for well focused imaging. Otherwise there will be image quality loss. However, sound velocity in the ocean varies with space and time, and there might also be measuring error of CTD (Conductivity, Temperature, and Depth) sensor, so sound velocity error has become one of the limiting factors to improve SAS resolution further. To characterize the effect of sound velocity error quantificationally, the practice SAS resolution is mode as the convolution of ideal seafloor reflectivity function and a phase error function in frequency domain, where the phase error is caused by incorrect sound velocity. Then the SAS resolution parameterized is calculated as a function of the sound velocity measuring error, or sound velocity gradient. It is shown that SAS azimuthally (along track) resolution loss, caused by sound velocity measurement error, increases linearly with detection range. Meanwhile the loss caused by sound velocity gradient increases squarely. It is simulated by considering the synthetic aperture data collection for a particular pixel, and results show that the point scatter response will defocus when the sound velocity measuring error is up to 1% at 200m range, or the sound velocity changes up to 2% over a typical gradient at 200m range, and be worse at a longer range. Furthermore, we demonstrate the influence of sound velocity errors on SAS imagery using a sea trial data and real CTD measurements at South China Sea. We evaluate the degradation in image quality with respect to sound velocity errors by using two plastic balls and a variable seafloor scene, and results also support the accuracy of theoretical conclusions above.

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Image-quality prediction of synthetic aperture sonar imagery

2010 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2010.5495165 ◽

2010 ◽

Cited By ~ 5

Author(s):

David P. Williams

Keyword(s):

Image Quality ◽

Synthetic Aperture ◽

Quality Prediction ◽

Synthetic Aperture Sonar

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A Versatile Algorithm for Autofocusing SAR Images

Journal of the Russian Universities Radioelectronics ◽

10.32603/1993-8985-2021-24-1-22-33 ◽

2021 ◽

Vol 24 (1) ◽

pp. 22-33

Author(s):

A. A. Monakov

Keyword(s):

Image Quality ◽

Objective Function ◽

Direct Method ◽

Phase Error ◽

Quadratic Function ◽

Radar Image ◽

Synthetic Aperture ◽

Objective Functions ◽

Radar Images ◽

Phase Errors

Introduction. Random deviations of the antenna phase centre of a synthetic aperture radar (SAR) are a source of phase errors for the received signal. These phase errors frequently cause blurring of the radar image. The image quality can be improved using various autofocus algorithms. Such algorithms estimate phase errors via optimization of an objective function, which defines the radar image quality. The image entropy and sharpness are well known examples of objective functions. The objective function extremum can be found by fast optimization methods, whose realization is a challenging computing task.Aim. To synthesize a versatile and computationally simple autofocusing algorithm allowing any objective function to used without changing its structure significantly.Materials and methods. An algorithm based on substituting the selected objective function with a simpler surrogate objective function, whose extremum can be found by a direct method, is proposed. This method has been referred as the MM optimization in scientific literature. It is proposed to use a quadratic function as a surrogate objective function.Results. The synthesized algorithm is straightforward, not requiring recursive methods for finding the optimal solution. These advantages determine the enhanced speed and stability of the proposed algorithm. Adjusting the algorithm for the selected objective function requires minimal software changes. Compared to the algorithm using a linear surrogate objective function, the proposed algorithm provides a 1.5 times decrease in the standard deviation of the phase error estimate, with an approximately 10 % decrease in the number of iterations.Conclusion. The proposed autofocusing algorithm can be used in synthetic aperture radars to compensate the arising phase errors. The algorithm is based on the MM-optimization of the quadratic surrogate objective functions for radar images. The computer simulation results confirm the efficiency of the proposed algorithm even in case of large phase errors.

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