Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (4): 714-722.doi: 10.21629/JSEE.2018.04.06

• Electronics Technology • Previous Articles     Next Articles

Fast PARAFAC decomposition with application to polarization sensitive array parameter estimations

Yang LI*()   

  • Received:2017-04-11 Online:2018-08-01 Published:2018-08-30
  • Contact: Yang LI E-mail:yangleecool@qq.com
  • About author:LI Yang was born in 1987. He received his B.S. degree and Ph.D. degree in electronic engineering from Fudan University, Shanghai, China, in 2009 and 2014 respectively. He is an algorithm engineer at the 10th Research Institute of China Electronics Technology Group Corporation, Chengdu. His research interests are in the areas of array signal processing, high-resolution parameter estimation, as well as numerical linear and multilinear algebra. E-mail: yangleecool@qq.com
  • Supported by:
    the National Natural Science Foundation of China(61571131);the Technology Innovation Fund of the 10th Research Institute of China Electronics Technology Group Corporation(H17038.1);This work was supported by the National Natural Science Foundation of China (61571131), and the Technology Innovation Fund of the 10th Research Institute of China Electronics Technology Group Corporation (H17038.1)

Abstract:

In tensor theory, the parallel factorization (PARAFAC) decomposition expresses a tensor as the sum of a set of rank-1 tensors. By carrying out this numerical decomposition, mixed sources can be separated or unknown system parameters can be identified, which is the so-called blind source separation or blind identification. In this paper we propose a numerical PARAFAC decomposition algorithm. Compared to traditional algorithms, we speed up the decomposition in several aspects, i.e., search direction by extrapolation, suboptimal step size by Gauss-Newton approximation, and linear search by n steps. The algorithm is applied to polarization sensitive array parameter estimation to show its usefulness. Simulations verify the correctness and performance of the proposed numerical techniques.

Key words: tensor decomposition, parallel factorization (PARAFAC), alternating least squares (ALS), polarization sensitive array (PSA)