An Improved Back-Projection Algorithm for GNSS-R BSAR Imaging Based on CPU and GPU Platform

Shiyu Wu,Zhichao Xu,Dongkai Yang,Feng Wang,Gongjian Guo

doi:10.3390/rs13112107

Abstract

Global Navigation Satellite System Reflectometry Bistatic Synthetic Aperture Radar (GNSS-R BSAR) is becoming more and more important in remote sensing because of its low power, low mass, low cost, and real-time global coverage capability. The Back Projection Algorithm (BPA) was usually selected as the GNSS-R BSAR imaging algorithm because it can process echo signals of complex geometric configurations. However, the huge computational cost is a challenge for its application in GNSS-R BSAR. Graphics Processing Units (GPU) provides an efficient computing platform for GNSS-R BSAR processing. In this paper, a solution accelerating the BPA of GNSS-R BSAR using GPU is proposed to improve imaging efficiency, and a matching pre-processing program was proposed to synchronize direct and echo signals to improve imaging quality. To process hundreds of gigabytes of data collected by a long-time synthetic aperture in fixed station mode, a stream processing structure was used to process such a large amount of data to solve the problem of limited GPU memory. In the improvement of the imaging efficiency, the imaging task is divided into pre-processing and BPA, which are performed in the Central Processing Unit (CPU) and GPU, respectively, and a pixel-oriented parallel processing method in back projection is adopted to avoid memory access conflicts caused by excessive data volume. The improved BPA with the long synthetic aperture time is verified through the simulation of and experimenting on the GPS-L5 signal. The results show that the proposed accelerating solution is capable of taking approximately 128.04 s, which is 156 times lower than pure CPU framework for producing a size of 600 m × 600 m image with 1800 s synthetic aperture time; in addition, the same imaging quality with the existing processing solution can be retained.

Highlights

The concept of Global Navigation Satellite System Reflectometry (GNSS-R) was firstly considered by Hall and Cordy in 1988 [1], which uses the reflected GNSS signals to obtain information about the Earth’s surface; they consider the technology to be less promising
Our research focuses on the use of Graphics Processing Units (GPU) to accelerate Back Projection Algorithm (BPA) in GNSS-R
Simulations were conducted to demonstrate the performance of the proposed algorithm, which were carried out using the Central Processing Unit (CPU) and GPU of the hardware platform described above, respectively

Summary

Introduction

The concept of GNSS-R was firstly considered by Hall and Cordy in 1988 [1], which uses the reflected GNSS signals to obtain information about the Earth’s surface; they consider the technology to be less promising. GNSS-R BSAR is one of the emerging applications of GNSS-R technology [19], which uses the GNSS satellites as non-cooperative illuminators and employs bistatic SAR technology to obtain an image of the Earth’s surface. This technology has been proven to be applicable to target detection and recognition [20], deformation monitoring [21,22], etc.

Methods

Results

Conclusion