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http://dx.doi.org/10.5370/KIEE.2017.66.7.1078

Optical Misalignment Cancellation via Online L1 Optimization  

Kim, Jong-Han (Agency for Defense Development)
Han, Yudeog (Agency for Defense Development)
Whang, Ick Ho (Agency for Defense Development)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.66, no.7, 2017 , pp. 1078-1082 More about this Journal
Abstract
This paper presents an L1 optimization based filtering technique which effectively eliminates the optical misalignment effects encountered in the squint guidance mode with strapdown seekers. We formulated a series of L1 optimization problems in order to separate the bias and the gradient components from the measured data, and solved them via the alternating direction method of multipliers (ADMM) and sparse matrix decomposition techniques. The proposed technique was able to rapidly detect arbitrary discontinuities and gradient changes from the measured signals, and was shown to effectively cancel the undesirable effects coming from the seeker misalignment angles. The technique was implemented on embedded flight computers and the real-time operational performance was verified via the hardware-in-the-loop simulation (HILS) tests in parallel with the automatic target recognition algorithms and the intra-red synthetic target images.
Keywords
L1 Optimization; Alternating Direction Method of Multipliers; Optical Misalignment Cancellation;
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  • Reference
1 S. Boyd, N. Parikh, E. Chu, B. Pelato, and J. Eckstein, "Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers," Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1-122, 2010.   DOI
2 S. Boyd, and L. Vandenverghe, Convex Optimization, Cambridge University Press, 2004.
3 T. A. Davis, Direct Methods for Sparse Linear Systems, SIAM, 2006.
4 S.-J. Kim, K. Koh, S. Boyd, and D. Gorinevsky "L1 Trend Filtering," SIAM Review, vol. 51, no. 2, pp. 339-360, 2009.   DOI
5 K. Koh, S.-J. Kim, and S. Boyd, "An Interior-point Method for Large-scale L1-regularized logistic regression," Journal of Machine Learning Research, vol. 1, no. 8, pp. 1519-1555, 2007.
6 R. T. Rockafellar, Convex Analysis, Princeton University Press, 1970.
7 Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, "Orthogonal Matching Pursuit: Recursive Function Approximation with Application to Wavelet Decomposition," in Proc. 27th Asilomar Conference on Signals, Systems and Computers, pp. 40-44, Nov. 1993.