TY - JOUR
T1 - Generalized forward/backward subaperture smoothing techniques for sample starved STAP
AU - Unnikrishna Pillai, S.
AU - Kim, Younglok L.
AU - Guerci, Joseph R.
N1 - Funding Information:
Manuscript received February 22, 1999; revised August 25, 2000. This work was supported in part by the Office of Naval Research under Contract N-00014-89-J-1512P-5. The associate editor coordinating the review of this paper and approving it for publication was Dr. Sergio Barbarossa. S. U. Pillai is with the Department of Electrical Engineering, Polytechnic University, Brooklyn, NY 11202 USA (e-mail: [email protected]). Y. L. Kim is with InterDigital Communications Corp., Melville, NY 11747 USA. J. R. Guerci is with the Defense Advanced Research Projects Agency (DARPA), Arlington, VA 22203 USA. Publisher Item Identifier S 1053-587X(00)10146-1.
PY - 2000/12
Y1 - 2000/12
N2 - A major issue in space-time adaptive processing (STAP) for moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the requisite interference covariance matrix is inadequate, thereby precluding STAP beamforming utilizing many adaptive degrees-of-freedom (DOFs). Although deterministic rank-reduction methods can reduce sample support requirements, they are invariably suboptimal from a signal-to-interference-plus-noise-ratio (SINR) standpoint. In this paper, a new generalized subspatial and subtemporal aperture smoothing method employing forward and backward data vectors is introduced to overcome the data deficiency problem. It is shown that multiplicative improvement in data samples can be obtained at the expense of negligible loss in space-time aperture of the steering vector.
AB - A major issue in space-time adaptive processing (STAP) for moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the requisite interference covariance matrix is inadequate, thereby precluding STAP beamforming utilizing many adaptive degrees-of-freedom (DOFs). Although deterministic rank-reduction methods can reduce sample support requirements, they are invariably suboptimal from a signal-to-interference-plus-noise-ratio (SINR) standpoint. In this paper, a new generalized subspatial and subtemporal aperture smoothing method employing forward and backward data vectors is introduced to overcome the data deficiency problem. It is shown that multiplicative improvement in data samples can be obtained at the expense of negligible loss in space-time aperture of the steering vector.
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U2 - 10.1109/78.887049
DO - 10.1109/78.887049
M3 - Article
AN - SCOPUS:0034511563
SN - 1053-587X
VL - 48
SP - 3569
EP - 3574
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 12
ER -