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A study of STAP in Nonhomogeneous Environments

A study of STAP in Nonhomogeneous Environments. R. S. Blum EECS Dept. Lehigh University . This material is based upon work supported by the Air Force Research Laboratory. R. S. Blum’s Grad. students. F. Golbasi K. McDonald Y. Zhang Z. Lin W. Xu Z. Zhang Z. Gu. Topics.

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A study of STAP in Nonhomogeneous Environments

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  1. A study of STAP in Nonhomogeneous Environments R. S. Blum EECS Dept. Lehigh University This material is based upon work supported by the Air Force Research Laboratory.

  2. R. S. Blum’s Grad. students F. Golbasi K. McDonald Y. Zhang Z. Lin W. Xu Z. Zhang Z. Gu

  3. Topics • PASTAP performance with MCARM data. • STAP using prior knowledge. • Closed-form expressions for performance analysis in nonhomogeneous cases.

  4. Topic 1:PASTAP Performance with MCARM Data.Collaboration with M. C. Wicks and W. L. MelvinAFRL and Georga Tech.

  5. STAP Algorithms Considered • ADPCA • Factored Post Doppler (FTS) • Extended Factored Approach (EFA) • Joint-domain Localized Approach (JDL) • Subarraying ADPCA (BDPCA) • Subarraying EFA (BEFA) • Subarraying FTS (BFTS) • Beamspace ADPCA (BeamAD)

  6. Real data Performance • MCARM flight 5 acq. 575 • Insert target, Amp 0.05, given Ang & Dop • Use Normalized (CFAR) test stat. • compare Mag at target to neighbor • Use Q neighboring range cells to estimate Covariance matrix

  7. Norm. Test Stat. - Range 150 • BeamAD • JDL • BEFA

  8. Conclusions for Topic 1 • JDL and EFA usually best or near best. • Subarraying EFA next best. • Post Doppler processing important? • ADPCA best in a few cases (just for nonhomogeneous cases).

  9. Topic 2:STAP using Prior Knowledge

  10. STAP using Prior Knowledge STAP SCHEME Radar returns Decisions Reduce number of parameters to be estimated Knowledge and models of jammers Knowledge and models of clutter

  11. Numerical results • Compare modified (using prior knowledge) to traditional scheme • Representative case: SMI Range bin 415 Target spatial Freq. 0.164 Norm Doppler 0.078, 0.156, 0.312 Amp 0.05

  12. Numerical results - NDF:0.078 • Traditional • Modified

  13. Numerical results - NDF:0.156 • Traditional • Modified

  14. Numerical results - NDF:0.312 • Traditional • Modified

  15. Conclusions for Topic 2: • Modified scheme generally very good for nonhomogeneous cases • Especially when target near clutter ridge • Largest improvement for SMI, ADPCA. Significant improvement for EFA and JDL, but not as large. • Can apply to other Schemes • Can consider other knowledge

  16. Topic 3: Analysis of STAP Algorithms for Cases with Mismatch

  17. N dimensional vectors Observations: • Cell under test: • Mismatch • Secondary data: • Independent • 0 if signal absent • Mismatch • Covariance Est.

  18. Test Statistic • > • < • q: steering mismatch • : sensitivity parameter • =0: MSMI or AMF • =1: GLR •  as  const: ACE

  19. Airborne Radar Example

  20. Airborne Radar Example

  21. Steering offset Covar mismatch

  22. Conclusions for Topic 3: • Have obtained closed form expressions for performance with mismatch • Tells which types of mismatch are important and which are not • Steering vector mismatch can offset covariance matrix mismatch

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