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

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      Mathematical Problems in Engineering
      Hindawi Limited

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          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).

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            Subspace-Augmented Clutter Suppression Technique for STAP Radar

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                Author and article information

                Journal
                Mathematical Problems in Engineering
                Mathematical Problems in Engineering
                Hindawi Limited
                1024-123X
                1563-5147
                2017
                2017
                : 2017
                :
                : 1-9
                Article
                10.1155/2017/1424835
                1d51e7f1-f718-415e-879a-e969ec6b14ae
                © 2017

                http://creativecommons.org/licenses/by/4.0/

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