Abstract
Synthetic Aperture Radar is a form of radar widely used to extract information about the surface of the target. The transformation of the signals into an image is based on DSP algorithms that perform intensive but repetitive computation over the signal data. Traditionally, an aircraft or satellite acquires the radar data streams and sends it to be processed on a data center to produce images faster. However, there are novel applications demanding on-board signal processing to generate images. This paper presents a novel implementation for an on-board embedded SoC of an accelerator for the Backprojection algorithm, which is the reference algorithm for producing images of SAR sensors. The methodology used is based on a HW/SW design partition, where the most time consuming computations are implemented in hardware. The accelerator was specified in HLS, which allows to reuse the code from the original implementation of the algorithm in software. The accelerator performs the computations using floating-point arithmetic to produce the same output as the original algorithm. The target SoC device is a Zynq 7020 from Xilinx which has a dual-core ARM-A9 processor along with a reconfigurable fabric which is used to implement the hardware accelerator. The proposed systems outperformed the software-only implementation in 7.7\(\times \) while preserving the quality of the image by adopting the same floating-point representations from the original software implementation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Change history
25 March 2020
The funding information was missing from the originally published chapter. This was corrected and the funding information was added.
References
Barker, K., et al.: PERFECT (Power Efficiency Revolution For Embedded Computing Technologies) Benchmark Suite Manual. Pacific Northwest National Laboratory and Georgia Tech Research Institute, December 2013. http://hpc.pnnl.gov/projects/PERFECT/
Cruz, H., Duarte, R.P., Neto, H.: Fault-tolerant architecture for on-board dual-core synthetic-aperture radar imaging. In: Hochberger, C., Nelson, B., Koch, A., Woods, R., Diniz, P. (eds.) ARC 2019. LNCS, vol. 11444, pp. 3–16. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17227-5_1
Gocho, M., Oishi, N., Ozaki, A.: Distributed parallel backprojection for real-time stripmap SAR imaging on GPU clusters. In: Proceedings - IEEE International Conference on Cluster Computing, ICCC 2017, September, pp. 619–620 (2017). https://doi.org/10.1109/CLUSTER.2017.64
Lentaris, G., et al.: High-performance embedded computing in space: evaluation of platforms for vision-based navigation. J. Aerosp. Inf. Syst. 15(4), 178–192 (2018). https://doi.org/10.2514/1.I010555
Pritsker, D.: Efficient global back-projection on an FPGA. In: 2015 IEEE Radar Conference (RadarCon), pp. 0204–0209, May 2015. https://doi.org/10.1109/RADAR.2015.7130996
Song, X., Yu, W.: Processing video-SAR data with the fast backprojection method. IEEE Trans. Aerosp. Electron. Syst. 52(6), 2838–2848 (2016). https://doi.org/10.1109/TAES.2016.150581
Wielage, M., Cholewa, F., Riggers, C., Pirsch, P., Blume, H.: Parallelization strategies for fast factorized backprojection SAR on embedded multi-core architectures. In: 2017 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS), pp. 1–6. IEEE, November 2017. https://doi.org/10.1109/COMCAS.2017.8244770
Acknowledgement
This work was supported by national funds through FundaĂ§Ă£o para a CiĂªncia e a Tecnologia (FCT) with references UID/CEC/50021/2019 and PTDC/EEI-HAC/31819/2017 (SARRROCA). HC would like to acknowledge FundaĂ§Ă£o para a CiĂªncia e a Tecnologia for the support through grant SFRH/BD/144133/2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Duarte, R.P., Cruz, H., Neto, H. (2020). Reconfigurable Accelerator for On-Board SAR Imaging Using the Backprojection Algorithm. In: RincĂ³n, F., Barba, J., So, H., Diniz, P., Caba, J. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2020. Lecture Notes in Computer Science(), vol 12083. Springer, Cham. https://doi.org/10.1007/978-3-030-44534-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-44534-8_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44533-1
Online ISBN: 978-3-030-44534-8
eBook Packages: Computer ScienceComputer Science (R0)