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Proceedings Paper

Flutter signal extracting technique based on FOG and self-adaptive sparse representation algorithm
Author(s): Jian Lei; Xiangtao Meng; Zheng Xiang
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Paper Abstract

Due to various moving parts inside, when a spacecraft runs in orbits, its structure could get a minor angular vibration, which results in vague image formation of space camera. Thus, image compensation technique is required to eliminate or alleviate the effect of movement on image formation and it is necessary to realize precise measuring of flutter angle. Due to the advantages such as high sensitivity, broad bandwidth, simple structure and no inner mechanical moving parts, FOG (fiber optical gyro) is adopted in this study to measure minor angular vibration. Then, movement leading to image degeneration is achieved by calculation. The idea of the movement information extracting algorithm based on self-adaptive sparse representation is to use arctangent function approximating L0 norm to construct unconstrained noisy-signal-aimed sparse reconstruction model and then solve the model by a method based on steepest descent algorithm and BFGS algorithm to estimate sparse signal. Then taking the advantage of the principle of random noises not able to be represented by linear combination of elements, useful signal and random noised are separated effectively. Because the main interference of minor angular vibration to image formation of space camera is random noises, sparse representation algorithm could extract useful information to a large extent and acts as a fitting pre-process method of image restoration. The self-adaptive sparse representation algorithm presented in this paper is used to process the measured minor-angle-vibration signal of FOG used by some certain spacecraft. By component analysis of the processing results, we can find out that the algorithm could extract micro angular vibration signal of FOG precisely and effectively, and can achieve the precision degree of 0.1".

Paper Details

Date Published: 25 October 2016
PDF: 9 pages
Proc. SPIE 10158, Optical Communication, Optical Fiber Sensors, and Optical Memories for Big Data Storage, 101581J (25 October 2016); doi: 10.1117/12.2247439
Show Author Affiliations
Jian Lei, China Academy of Aerospace Electronics Technology (China)
Xiangtao Meng, China Academy of Aerospace Electronics Technology (China)
Zheng Xiang, China Academy of Aerospace Electronics Technology (China)


Published in SPIE Proceedings Vol. 10158:
Optical Communication, Optical Fiber Sensors, and Optical Memories for Big Data Storage

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