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Mathematical Problems in Engineering
Volume 2017, Article ID 1424835, 9 pages
https://doi.org/10.1155/2017/1424835
Research Article

A Novel Interference Detection Method of STAP Based on Simplified TT Transform

Air and Missile Defense College, Air Force Engineering University, Xi’an, Shaanxi 710051, China

Correspondence should be addressed to Qiang Wang; moc.qq@3811169101

Received 20 June 2017; Accepted 26 October 2017; Published 21 November 2017

Academic Editor: Fazal M. Mahomed

Copyright © 2017 Qiang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Training samples contaminated by target-like signals is one of the major reasons for inhomogeneous clutter environment. In such environment, clutter covariance matrix in STAP (space-time adaptive processing) is estimated inaccurately, which finally leads to detection performance reduction. In terms of this problem, a STAP interference detection method based on simplified TT (time-time) transform is proposed in this letter. Considering the sparse physical property of clutter in the space-time plane, data on each range cell is first converted into a discrete slow time series. Then, the expression of simplified TT transform about sample data is derived step by step. Thirdly, the energy of each training sample is focalized and extracted by simplified TT transform from energy-variant difference between the unpolluted and polluted stage, and the physical significance of discarding the contaminated samples is analyzed. Lastly, the contaminated samples are picked out in light of the simplified TT transform-spectrum difference. The result on Monte Carlo simulation indicates that when training samples are contaminated by large power target-like signals, the proposed method is more effective in getting rid of the contaminated samples, reduces the computational complexity significantly, and promotes the target detection performance compared with the method of GIP (generalized inner product).