PROJECT TITLE :
Robust space-time adaptive processing for nonhomogeneous clutter in the presence of model errors
In this paper, we 1st develop a completely unique array self-calibration technique for estimating array gain-part errors by computing the muddle subspace from the radar system parameters and using the clutter knowledge in area-time adaptive processing (STAP). The proposed algorithm is shown to perform well even in nonhomogeneous muddle, and it can improve the performance of existing STAP algorithms, like the muddle subspace-based mostly method within the presence of array gain-part errors.We have a tendency to conjointly develop a two-stage STAP approach for suppressing nonhomogeneous clutter in the presence of model errors in addition to array gain-phase errors. In our 2-stage STAP approach, the first stage explores the litter subspace calculated from the radar system parameters to suppress the main muddle. The second stage employs the traditional method, like the partially adaptive sample matrix inversion STAP method, to remove any residual litter. Numerical results illustrate the advantages of the array self-calibration method and therefore the effectiveness of the two-stage STAP methodology. Finally, the performance of the three STAP strategies is compared via the well-known MCARM information set. The results additional confirm that there's an improvement in performance when using array self-calibration along with the 2-stage STAP method.
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