Name
Abstract | ||
|---|---|---|
With the development of satellite load technology and very large-scale integrated (VLSI) circuit technology, onboard real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. The state-of-the-art System-on-Programmable-Chip (SoPC) technique, associated with embedded processor and other function modules, provides a potential solution to satisfy all constraints. However, with the improvement of processing efficiency and imagery granularity, to implement an entire SAR imaging processing using floating-point arithmetic is unaffordable. Data fixed-pointing is an effective solution, and the core issue is the finite word length optimization under the condition of trading-off hardware resource and processing precision. In this paper, we analyze the finite word length computing error for SAR imaging system using Chirp Scaling (CS) algorithm, and propose a mathematical computing error model. Then, the empirical formula of the system's output noise-to-signal ratio is derived. Guiding by the software simulation result, we implement and verify the proposed method into a Zynq+NetFPGA platform. The run-time results show that the proposed method can achieve a decent image quality assessed by Integrated Side Lobe Ratio (ISLR), Peak Side Lobe Ratio (PSLR) and Relative Mean Square Deviation (RMSD). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/HPEC.2018.8547564 | 2018 IEEE High Performance extreme Computing Conference (HPEC) |
Keywords | Field | DocType |
Synthetic Aperture Radar (SAR) Imaging,Finite Word Length,FPGA,Fixed-point Analysis | Synthetic aperture radar,Computer science,Image quality,Real-time computing,Root-mean-square deviation,Side lobe,Fixed point,Granularity,NetFPGA,Very-large-scale integration | Conference |
ISSN | ISBN | Citations |
2377-6943 | 978-1-5386-5990-8 | 0 |
PageRank | References | Authors |
0.34 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingyi Li | 1 | 4 | 1.62 |
| Changjin Li | 2 | 0 | 0.34 |
| Yizhuang Xie | 3 | 10 | 3.64 |
| Liang Chen | 4 | 19 | 4.18 |
| Hao Shi | 5 | 30 | 9.58 |
| Yi Deng | 6 | 28 | 4.13 |