A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:64079", author = "R. Fan and Q. Wan and H. Chen and Y.L. Liu and Y.P. Liu", title = "A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model", 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.", volume = "7", number = "5", pages = "671-5", }
