Covariance Matrix Estimation Depending on Bias Correction of the Sample Eigenvalues for STAP

Covariance matrix estimation is an important step for space-time adaptive processing (STAP) in radar. The sample covariance matrix can be used as an estimate of the covariance matrix. However, for this method, it is hard to obtain a good performance in the limited sample situation. To solve this problem, the diagonal loading algorithms are usually utilized. Nonetheless, instead of treating different sample eigenvalue separately, the diagonal loading algorithms correct all the sample eigenvalues with the same parameter. To further improve the performance of the diagonal loading algorithm, this paper proposes a covariance matrix estimation method depending on bias correction of the sample eigenvalues for STAP, which corrects the sample eigenvalue bias depending on the sample eigenvalue. Simulation results shows that the proposed algorithm outperforms the diagonal loading algorithm in the limited sample condition.