Author: Pat Lasserre
Synthetic Aperture Radar (SAR) has many important applications, for example: military surveillance; border security; and monitoring environmental issues like deforestation, flooding, oil spills, changes in arctic sea ice, and wildfire progression.
Back projection is considered the ideal SAR algorithm, but its adoption has been limited to date because of the computational expense of running it on CPU or GPU.
In this post, we’ll present a solution that addresses the computational expense of back projection — allowing it to be more widely adopted.
SAR is a form of radar that uses microwaves to create high-resolution images.
SAR radars are mounted on moving airborne platforms, creating the effect of a large virtual antenna that provides high resolution. The radars emit microwaves towards a surface, and the intensity of the reflected waves is used to create the images.
Two key advantages that SAR sensors have over optical sensors is that they can work both day and night and in all kinds of weather (with cloud penetration being particularly advantageous).
To create an image, optical sensors (passive) measure solar light reflected from objects. That means that they will have issues with darkness, adverse weather conditions, clouds, smoke, etc. As noted in this paper, “such limitations produce an information gap during or post a disaster event delaying the planning and the course of action needed to save lives.”
As noted in this post, SAR sensors don’t suffer from these issues because the lengths of the microwaves used by SAR “are much longer than those of visible or infrared light, making the sensors functional in all lighting conditions and able to penetrate through clouds and dust.”
SAR can also work on many moving platforms with diverse imaging areas, resolution ranges, and imaging ranges. For example, it can work on imaging ranges from 1000km (satellites) to 100m (quadcopters). Resolution, which is the area covered by an individual image pixel, can range from 100 meters per pixel for huge areas like oceans or deserts to sub meter per pixel for high-accuracy applications covering small and medium fixed areas.
For SAR applications that require high resolution, SAR spotlight-mode is often used. It works by transmitting successive waves at a fixed target over a limited area as the antenna moves past the target.
Back projection is the preferred SAR processing algorithm because of its many benefits:
· Supports a wide range of frequencies, including low frequencies, which provide better penetration into the target
· Supports low imaging range which is good for platforms like quadcopters and unmanned aerial vehicle (UAV)
· Compensates for platform motion with high maneuverability — good for quadcopters and UAV
Its adoption, however, has been limited by the computational expense of running it on traditional solutions based on CPU.
Creating a SAR image with the back projection algorithm takes a lot of computation. It can take thousands of pulses to create an image, and solutions that use CPU work sequentially, pixel-by-pixel in the grid of each pulse.
Taking that sequential approach on thousands of pulses carries a high computational expense that leads to prohibitive costs and power usage.
A better approach is to work on all the pixels in the grid for each pulse in parallel. That is what GSI Technology’s APU does.
By enabling parallel processing, the APU allows back-projection-based SAR images to be produced cost-effectively and power-efficiently. It also reduces the processing time from a few minutes to a few seconds.
For the case of a large area SAR image being processed in one second at high resolution, the APU uses, on average, 88% less power than CPU or GPU systems.
Another benefit that the APU brings to back projection is its ability to efficiently handle non-linear functions such as COS, SIN, SQRT, and table lookup, which are key functions of back projection. CPUs and GPUs do not efficiently handle non-linear operations.
For your next SAR project, look for a solution that provides parallel processing and non-linear arithmetic support — so you can take advantage of back projection, the ideal SAR image-formation algorithm.