A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model

Authors: R. Fan, Q. Wan, H. Chen, Y.L. Liu, Y.P. Liu

Abstract:

In the paper, a fast high-resolution range profile synthetic algorithm called orthogonal matching pursuit with sensing dictionary (OMP-SD) is proposed. It formulates the traditional HRRP synthetic to be a sparse approximation problem over redundant dictionary. As it employs a priori that the synthetic range profile (SRP) of targets are sparse, SRP can be accomplished even in presence of data lost. Besides, the computation complexity decreases from O(MNDK) flops for OMP to O(M(N + D)K) flops for OMP-SD by introducing sensing dictionary (SD). Simulation experiments illustrate its advantages both in additive white Gaussian noise (AWGN) and noiseless situation, respectively.

Keywords: GTD-based model, HRRP, orthogonal matching pursuit, sensing dictionary.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084354

References:

[1] A. Zyweck and R. E. Bogner, "High-resolution radar imagery of the Mirage III aircraft," IEEE Trans. Antennas Propag., vol. 42, no. 9, pp. 1356-1360, Sep. 1994.
[2] H.Y. Chen, Y.X., Liu, X. Li, and G.R Guo, "Mathematics of Synthesizing Range Profile," IEEE Trans. Signal Processing, vol. 55, no. 5, pp. 1950- 1955, May 2007.
[3] L. Du, H.W. Liu, and Z. Bao, "Radar HRRP Statistical Recognition: Parametric Model and Model Selection," IEEE Trans. Signal Processing, vol. 56, no. 5, pp. 1931-1944, May 2008.
[4] Y.M. Liu, H.D. Meng, G. Li, and X.Q. Wang, "Velocity Estimation and Range Shift Compensation for High Range Resolution Profiling in Stepped-Frequemcy Radar," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 791-795, Oct. 2010.
[5] Y.C. Li, L. Zhang, B.C. Liu, Y. H. Quan, M.D. Xing, and Z. Bao, "Stepped-frequency inverse synthetic aperture radar imaging based on adjacent pulse correlation integration and coherent processing," IET Signal Processing, vol.5, no.7, pp. 632-642, 2011.
[6] P. Mian, L. Du, P.H. Wang, H.W. Liu, and Z. Bao, "Noise-Robust Modification Method for Gaussian-Based Models With Application to Radar HRRP Recognition," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 3, pp. 558-562, May. 2013.
[7] S.S. Chen, D.L Donoho, and M.A. Saunders, "Atomic decomposition by basis pursuit," SIAM J. Appl. Math., vol. 43, no. 1, pp. 129-159, 2001.
[8] E.J. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
[9] E.J. Candes and T. Tao, "Decoding by linear programming," IEEE Trans. Inform. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005.
[10] D.L. Donoho and X. Huo, "Uncertainty principles and ideal atomic decomposition," IEEE Trans. Inform. Theory, vol. 47, no. 7, pp. 2845- 2862, Nov. 2001.
[11] J.A. Troop, "Greed is good: algorithmic results for sparse approximation," IEEE Trans. Inform. Theory, vol. 50, no. 10, pp. 2231-2242, Oct. 2004.
[12] K. Schnass and P. Vandergheynst, "Dictionary preconditioning for greedy algorithms," IEEE Trans. Signal Processing, vol. 56, no. 5, pp. 1994-2002, May 2008.
[13] R.M. Yang, Q. Wan, and W.L. Yang, "Greedy approach to sparse multipath channel estimation using sensing dictionary," Int. J. of Adaptive Control and Signal Processing, vol. 25, no. 6, pp. 544-553, Jun. 2011.
[14] L.C. Potter, D.M. Chiang, R. Carrire, and M.J. Gerry, "A GTD-based parametric model for radar scattering," IEEE Trans. Antennas Propag., vol. 43, no. 10, pp. 1058-1067, Oct. 1995.
[15] D.X. Jin, S.M. Cui, and D.G. Fang, "An efficient representation for radar scattering with the use of the genetic algorithm," Microw OPT Techn. Lett., vol 15, no. 1, pp. 36-39, May 1997.
[16] Z.L. Yang, D.G. Fang, W.X. Sheng, T.J. Liu, and J. Zhuang, "Frequency extrapolation by genetic algorithm based on GTD model for radar cross section," in Proc. 5th Int. Symposium on Antennas, Propagation and EM Theory Peking (China), 2000, pp. 569-572.
[17] T.T. Cai, L. Wang, "Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise," IEEE Trans. Inform. Theory, vol. 57, no. 7, pp. 4680-4688, Jul. 2011.