Synthetic Aperture Sonar Image Contrast Prediction

Sidescan imaging sonar performance is often described using metrics such as resolution, maximum range, and area coverage rate. These attributes focus on describing how finely and over what area a sensor can make imagery of the seafloor and targets. While these metrics are important, they are inadequate for fully characterizing the quality of sonar imagery. Shadow regions often carry as much, and sometimes even more, information than the direct return from a target. The shadow contrast within an image is therefore a critical property, and it is the result of a complex interaction between the sonar hardware, the environment, and the signal processing. This paper builds on key results from the synthetic aperture radar and sonar literature to develop a comprehensive, quantitative model for predicting the shadow contrast ratio. A model for the contrast ratio is constructed using an approach that is similar to the development of the sonar equation. This ratio describes the average relative level between the seafloor and a shadow. The model includes the effects of the transducer and array design, ambient noise, quantization noise, unwanted volume and surface reverberation, and the signal processing used to construct the synthetic aperture sonar (SAS) imagery. The shadow contrast predicted by the model is compared to the contrast measured from SAS imagery, where close agreement is observed. Examples are presented where the model is applied to a hypothetical high-frequency imaging SAS sensor in a typical operating environment. The shadow contrast ratio is estimated as a function of the sensor range and key parameters of interest, such as receiver channel spacing, sediment backscatter coefficient, and the time-bandwidth product of the transmitted signal. At long ranges, the contrast ratio is limited by additive noise interference, while at near ranges the effect of the receiver channel spacing dominates the contrast ratio through along-track ambiguities. The use of this tool for analysis and prediction of image quality has significant implications for system design, mission planning, and data processing.