Two-dimensional scattering centre extraction based on multi-input multi-output radars

This article presents two-dimensional (2-D) scattering centres' parameter estimation based on linear uniform multi-input multi-output (MIMO) radars. Unlike single-input single-output (SISO) radars, MIMO radars transmit multiple detection signals at the same time. We assume that each antenna of a MIMO radar transmits signals with a set of different spread spectrum codes so as to distinguish the echoes from all antennas. Then, with spread spectrum and de-spread spectrum techniques, each antenna of a MIMO radar could distinguish its own stepped-up frequency echoes transmitted by itself. In this article, we take m sequences as spread spectrum codes and assume that the ratio between the MIMO radars array aperture and the target distance is much less than 1, then we use 2-D fast Fourier transform (FFT) and matrix extended matrix pencil (MEMP) methods to extract 2-D scattering centres' parameters after MIMO radar have received de-spread echoes. Simulations illustrate that both SISO radars and linear uniform MIMO radars using the spread spectrum technique can get similar estimation results, and the MEMP method can afford super-resolution estimation results, whereas the 2-D FFT method is more useful when the number of MIMO radar array elements are no more than the number of scattering centres.

[1]  Xiao Shun-ping,et al.  Fully polarized radar target scattering center extraction , 2007, 2007 1st Asian and Pacific Conference on Synthetic Aperture Radar.

[2]  Hao Ling,et al.  Three-dimensional scattering center extraction using the shooting and bouncing ray technique , 1996 .

[3]  Lawrence Carin,et al.  Identification of ground targets from sequential high-range-resolution radar signatures , 2002 .

[4]  Emanuel Radoi,et al.  Advances in subspace eigenanalysis based algorithms: from 1D toward 3D superresolution techniques , 2001, 5th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service. TELSIKS 2001. Proceedings of Papers (Cat. No.01EX517).

[5]  E. Radoi,et al.  Some radar imagery results using superresolution techniques , 2004, IEEE Transactions on Antennas and Propagation.

[6]  Zheng Bao,et al.  Multi-aspect radar target recognition method based on scattering centers and HMMs classifiers , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[7]  R. Kumaresan,et al.  Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise , 1982 .

[8]  B. D. Steinberg,et al.  Reduction of sidelobe and speckle artifacts in microwave imaging: the CLEAN technique , 1988 .

[9]  Rick S. Blum,et al.  MIMO radar waveform design based on mutual information and minimum mean-square error estimation , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Jun Li,et al.  Joint DOD and DOA estimation for bistatic MIMO radar , 2009, Signal Process..

[11]  Zheng Bao,et al.  Multi-aspect radar target recognition method based on scattering centers and HMMs classifiers , 2005 .

[12]  Hyo-Tae Kim,et al.  Radar target identification using one-dimensional scattering centres , 2001 .

[13]  Xiaofei Zhang,et al.  Improved coherent DOA estimation algorithm for uniform linear arrays , 2009 .

[14]  Stéphanie Rouquette-Léveil,et al.  Estimation of frequencies and damping factors by two-dimensional ESPRIT type methods , 2001, IEEE Trans. Signal Process..

[15]  Zhao Hongzhong,et al.  Global Scattering Center Model Extraction of Radar Targets Based on Wideband Measurements , 2008, IEEE Transactions on Antennas and Propagation.

[16]  Noh Hoon Myung,et al.  Efficient technique for two-dimensional scattering center extraction and ISAR image formation , 2008 .

[17]  P. P. Vaidyanathan,et al.  MIMO Radar Ambiguity Properties and Optimization Using Frequency-Hopping Waveforms , 2008, IEEE Transactions on Signal Processing.