利用加权联合协方差矩阵拟合进行干涉相位估计

提出一种基于加权联合协方差矩阵拟合的干涉相位估计方法。该方法充分利用包含在联合协方差矩阵中的相干信息,并采用加权联合协方差矩阵拟合代替特征分解进行干涉相位估计,避免了相位估计过程中信号子空间扩散的影响。通过给出干涉相位闭式解的形式,解决了角度搜索导致的大运算量问题。仿真数据和实测数据的处理结果证明了该方法的有效性和稳健性。

Interferometric Phase Estimation Based on Weighted Joint Covariance Matrix Fitting

A novel method based on the weighted joint covariance matrix fitting for synthetic aperture radar interferometry is presented. The method takes advantage of the coherence information of the neighboring pixel pairs and makes use of the interferometric information embedded in a joint covariance matrix, which makes it possible to estimate the interferometric phase in the presence of large coregistration errors, even up to one pixel. Benefiting from weighting on the joint covariance matrix and employing matrix fitting to estimate the interferometric phase instead of eigendecomposition, the method does not need to calculate the signal subspace dimension, thus avoiding the effect of the rank variation of the signal subspace. A fast algorithm for the interferometric phase estimation is proposed in which the closed-form solution to the interferometric phase is directly obtained, for the angle scanning search based method is time-consuming. The results of numerical simulations and real data demonstrate the validation of the proposed method.