A Robust State Space Model for the Characterization of Extended Returns in Radar Target Signatures

Analysis of radar scattering from targets with curved boundaries, such as objects comprising cylindrical and conical shapes, is important to many aerospace applications. The radar return is composed of a well-characterized physical optics response in the illuminated region where the transmitter and receiver are not shadowed by the object, and a combination of modal responses (e.g., creeping waves and edge-diffracted fields) in the shadow region. The modal responses have longer down-range than scattering centers located on the object, and therefore, produce extended (or off-body) returns in ISAR images, which are not well-understood. However, these returns are strongly dependent on local features of the object, and thus contain valuable information with regard to the target's geometrical and physical composition. Multiple reflections from illuminated facets, as well as multiply diffracted waves, can also add coherently in the direction of the receiver and produce such returns. This paper applies a robust, coherent-processing system identification technique, originally developed for radar sensor fusion, to estimate amplitude and phase of the scatterers that characterize extended returns in the target signature. Examples are presented that highlight the extraction of creeping waves using measured data on a cone-sphere.

[1]  Xing Ping Lin,et al.  Application of spectral domain Prony's method to the FDTD analysis of planar microstrip circuits , 1994 .

[2]  L. Felsen,et al.  Radiation and scattering of waves , 1972 .

[3]  P. Siegel,et al.  A 155-GHz monolithic low-noise amplifier , 1998 .

[4]  Mats Viberg,et al.  Subspace-based methods for the identification of linear time-invariant systems , 1995, Autom..

[5]  Hao Ling,et al.  Multimode parameter extraction for multiconductor transmission lines via single-pass FDTD and signal-processing techniques , 1998 .

[6]  Jian Li,et al.  Super resolution SAR imaging via parametric spectral estimation methods , 1999 .

[7]  Kevin M. Cuomo,et al.  Super-resolution methods for wideband radar , 1992 .

[8]  J. Maciejowski,et al.  System identification using balanced parametrizations , 1997, IEEE Trans. Autom. Control..

[9]  Thomas Kailath,et al.  Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..

[10]  J. E. Piou Balanced Realization for 2-D Data Fusion** , 2005 .

[11]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[12]  K. Arun,et al.  State-space and singular-value decomposition-based approximation methods for the harmonic retrieval problem , 1983 .

[13]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[14]  H. Akaike A new look at the statistical model identification , 1974 .

[15]  Raj Mittra,et al.  A technique for extracting the poles and residues of a system directly from its transient response , 1975 .

[16]  L. Carin,et al.  On the superresolution identification of observables from swept-frequency scattering data , 1997 .

[17]  Joseph T. Mayhan,et al.  Ultra-Wideband Coherent Processing , 1998 .

[18]  Krishna Naishadham,et al.  State-space system representation of time-domain responses from electromagnetic simulations , 2005, IMS 2005.

[19]  P. M. Makila State space identification of stable systems , 1999 .

[20]  Raj Mittra,et al.  A combination of FD-TD and Prony's methods for analyzing microwave integrated circuits , 1991, Antennas and Propagation Society Symposium 1991 Digest.

[21]  Ilan Ziskind,et al.  Detection of the number of coherent signals by the MDL principle , 1989, IEEE Trans. Acoust. Speech Signal Process..

[22]  J. T. Mayhan,et al.  Ultrawide-band coherent processing , 1999 .

[23]  Robert L. Stevenson,et al.  Super-resolution from image sequences-a review , 1998, 1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268).

[24]  A. McCowen,et al.  An improved pencil-of-functions method and comparisons with traditional methods of pole extraction , 1987 .

[25]  K. Naishadham,et al.  State-space spectral estimation of characteristic electromagnetic responses in wideband data , 2005, IEEE Antennas and Wireless Propagation Letters.

[26]  A. Vegas,et al.  Computation of resonant frequencies and quality factors of open dielectric resonators by a combination of the finite-difference time-domain (FDTD) and Prony's methods , 1992, IEEE Microwave and Guided Wave Letters.

[27]  Kevin M. Cuomo,et al.  High resolution 3D "snapshot" ISAR imaging and feature extraction , 2001 .

[28]  Katsuhiko Ogata,et al.  Discrete-time control systems , 1987 .

[29]  Stuart R. DeGraaf,et al.  SAR imaging via modern 2-D spectral estimation methods , 1998, IEEE Trans. Image Process..

[30]  T. Itoh,et al.  Microwave structure characterization by a combination of FDTD and system identification methods , 1993, IEEE Microwave and Guided Wave Letters.

[31]  L. Z. Kennedy,et al.  Scattering by a Sphere , 2021, Time-Domain Scattering.

[32]  Bhaskar D. Mo Model Based Processing of Signals: A State Space Approach , 1992 .

[33]  M.L. Burrows,et al.  Two-dimensional ESPRIT with tracking for radar imaging and feature extraction , 2004, IEEE Transactions on Antennas and Propagation.

[34]  Krishna Naishadham,et al.  A NOVEL 1-D BLOCK PROCESSING APPROACH TO 2-D NMR SPECTROSCOPY , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[35]  J. E. Piou A State Identification Method for 1-D Measurements with Gaps** , 2005 .

[36]  Jie Yang,et al.  Interpolation/Extrapolation of Radar Cross-Section (RCS) Data in the Frequency Domain Using the Cauchy Method , 2007, IEEE Transactions on Antennas and Propagation.

[37]  Krishna Naishadham,et al.  ARMA-based time-signature estimator for analyzing resonant structures by the FDTD method , 2001 .

[38]  Tapan K. Sarkar,et al.  Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise , 1990, IEEE Trans. Acoust. Speech Signal Process..

[39]  Y. Hua,et al.  Generalized pencil-of-function method for extracting poles of an EM system from its transient response , 1989 .

[40]  Raj Mittra,et al.  Scattering center analysis via Prony's method , 1987 .