Onboard processing for SAR satellites is a growing area of research due to the increasing demand for Earth applications for Synthetic Aperture Radar (SAR) data.
The amount of data produced by a SAR sensor prevents real-time data transfer to the ground due to the limitations of downlink speeds, thus requiring large on-board data storage. Several high-level solutions have been proposed to improve this:
- Use specialised on-board compression algorithms.
- Use on-board Artificial Intelligence (AI) to filter irrelevant or low quality data and send only a subset of data.
These two solutions have a major drawback of needing significant on-board computer power, and therefore can compromise the overall power of the satellite.
Data processing approaches to SAR data
Several data processing solutions have been studied for data processing in-space and for SAR data processing in particular:
- The use of FPGAs, i.e., programable hardware, can reduce power consumption and increase data processing, however high complexity in programming is the main drawback of FPGA.
- GPUs are probably the fastest data processing processors today. However, large power consumption, thermal load and performance bottlenecks in data transfer to GPU memory reduce their appeal in-space applications.
- Software solutions in the host computer, are the most attractive solution due their programming simplicity and relatively good performance. However, power consumption is high and data processing performance is often not fast enough.
Klepsydra solution: more data processing with less power
Figure 1 shows comparison of these data processing solutions. These solutions, without a high performance data processing accompanying them, are unable to meet power budget and/or data processing requirements.
Klepsydra has developed an advanced software framework for edge computing applications providing best-in-class data processing performance whilst significantly reducing latency and power consumption. This software can be used standalone or combined with FPGA. In either case, Klepsydra outperforms standard data processing solutions for SAR satellites.
Figure 1. Comparative of data processing approaches