Abstract
Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm. The unique nonstationary characteristic of signal for space-based early warning radar (SBEWR) leads to the spatial variation of training sample and the insufficient number of optional independent identically distributed (i.i.d.) training samples, which brings difficulties to training sample selection and covariance matrix estimation. To improve the estimation accuracy of clutter covariance matrix and the performance of STAP for SBEWR in a heterogeneous environment, a novel training sample selection and clutter covariance matrix estimation method is proposed. The method based on clutter subspace reconstruction and spectrum correction technology can improve the estimation accuracy of clutter covariance matrix in the case of nonstationary signals and heterogeneous environments. The clutter covariance matrix estimated by the proposed method is similar to the clutter covariance matrix of the CUT, and the performance of STAP is improved. The experimental results confirm the performance of the proposed method.
Highlights
spatialtemporal adaptive processing (STAP) technology greatly improves the detect moving targets capability of radar in strong clutter and interference environments [1,2,3]
Based on the fitting degree between the eigenvalue spectrum of the covariance matrix of the training sample and the cell under test (CUT), a series of training samples meeting i.i.d. conditions are selected in the heterogeneous environment, and the clutter position information of each selected training sample is corrected to estimate the clutter covariance matrix of CUT
We avoid the influence of spatial variability on the evaluation of sample similarity in traditional methods, and avoid the performance loss caused by the spatial variability of clutter position information between training samples
Summary
STAP technology greatly improves the detect moving targets capability of radar in strong clutter and interference environments [1,2,3]. Based on the fitting degree between the eigenvalue spectrum of the covariance matrix of the training sample and the CUT, a series of training samples meeting i.i.d. conditions are selected in the heterogeneous environment, and the clutter position information of each selected training sample is corrected to estimate the clutter covariance matrix of CUT This proposed method can make up for the shortcomings of traditional methods in the no-stationary and heterogeneous environment, and greatly improves the clutter suppression and moving target detection performance of SBEWR in a heterogeneous environment. Besides due to the rotation of the earth, the received data of different range ambiguity positions deviates in the spectrum These factors lead to the broadening of the clutter power spectrum and introduce the spatial variability of spatialtemporal spectrum in the distance dimension, which brings difficulties to the estimation of the clutter covariance matrix. The adaptive weight vector is used as the weight of the second stage antenna pattern, that is, the weight of the channel data, can be expressed as: wsub
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