Abstract:

High frequency radar can only obtain one snapshot of targets in frequency domain during a coherent processing interval. It leads to a degraded estimation of direction of arrival (DOA). By comparing the eigen decomposition of the covariance matrix estimation method in temporal with that in frequency domain, a dimension-reduction method is used to estimate the covariance matrix with a single snapshot in frequency domain, in which a relatively higher signal-to-noise ratio (SNR) is achieved. An original data matrix is then constructed by the signal eigenvector correspond to the biggest eigenvalue after the eigen decomposition of the covariance matrix. The noise subspace is obtained by the singular value decomposition (SVD) of the data matrix. After that, a noise eigenvector is reconstructed by combining all the noise eigenvectors. Finally, the new noise eigenvector is used to perform the DOA estimation. Simulation and experiment data analysis demonstrate that the proposed algorithm outperforms both dimensionreduction Toeplitz method and forward-backward smoothing method on precision and resolution.