Abstract
High-resolution spectrum estimation has continually attracted great attention in spectrum reconstruction based on Fourier transform imaging spectroscopy (FTIS). In this paper, a parallel solution for interference data processing using high-resolution spectrum estimation is proposed to reconstruct the spectrum in a fast high-resolution way. In batch processing, we use high-performance parallel-computing on the graphics processing unit (GPU) for higher efficiency and lower operation time. In addition, a parallel processing mechanism is designed for our parallel algorithm to obtain higher performance. At the same time, other solving algorithms for the modern spectrum estimation model are introduced for discussion and comparison. We compare traditional high-resolution solving algorithms running on the central processing unit (CPU) and the parallel algorithm on the GPU for processing the interferogram. The experimental results illustrate that runtime is reduced by about 70% using our parallel solution, and the GPU has a great advantage in processing large data and accelerating applications.
Highlights
In recent years, with the rapid development of imaging spectroscopy, the Fourier transform spectrometer has become one of the most important payloads in space exploration and component analysis [1,2,3,4,5,6]
Measures are taken to divide light from the target into two coherent light beams, which will interfere on the sensor and change the optical path difference (OPD) of the two beams in order to obtain a series of interference patterns
We propose a parallel solution for high-resolution spectrum estimation
Summary
With the rapid development of imaging spectroscopy, the Fourier transform spectrometer has become one of the most important payloads in space exploration and component analysis [1,2,3,4,5,6]. Jian et al [14,15] brought modern spectrum estimation into the field of spectral reconstruction They introduced the multiple signal classification (MUSIC) [16] algorithm and the autoregressive (AR) [17,18] model for better performance in spectral recovery. These algorithms are good at spectrum reconstruction in resolution, but are very time consuming. This paper will focus on quick solutions of the autoregressive model for high spectral resolution with better performance in real time and the parallel processing scheme of spectrum reconstruction based on high performance parallel computing on the GPU.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
