Parameter estimation of GTD model and RCS extrapolation based on a modified 3D-ESPRIT algorithm

The noise robustness and parameter estimation performance of the classical three-dimensional estimating signal parameter via rotational invariance techniques (3D-ESPRIT) algorithm are poor when the parameters of the geometric theory of the diffraction (GTD) model are estimated at low signal-to-noise ratio (SNR). To solve this problem, a modified 3D-ESPRIT algorithm is proposed. The modified algorithm improves the parameter estimation accuracy by proposing a novel spatial smoothing technique. Firstly, we make cross-correlation of the auto-correlation matrices; then by averaging the cross-correlation matrices of the forward and backward spatial smoothing, we can obtain a novel equivalent spatial smoothing matrix. The formula of the modified algorithm is derived and the performance of this improved method is also analyzed. Then we compare root-mean-square-errors (RMSEs) of different parameters and the locating accuracy obtained by different algorithms. Furthermore, radar cross section (RCS) of radar targets is extrapolated. Simulation results verify the effectiveness and superiority of the modified 3D-ESPRIT algorithm.