Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information of urban infrastructures. Due to the widespread applications of the MUSIC algorithm in source localization, it is a suitable solution in TomoSAR when multiple snapshots (looks) are available. While the classical MUSIC algorithm aims to estimate the whole reflectivity profile of scatterers, sequential MUSIC algorithms are suited for the detection of sparse point-like scatterers. In this class of methods, successive cancellation is performed through orthogonal complement projections on the MUSIC power spectrum. In this work, a new sequential MUSIC algorithm named recursive covariance canceled MUSIC (RCC-MUSIC), is proposed. This method brings higher accuracy in comparison with the previous sequential methods at the cost of a negligible increase in computational cost. Furthermore, to improve the performance of RCC-MUSIC, it is combined with the recent method of covariance matrix estimation called correlation subspace. Utilizing the correlation subspace method results in a denoised covariance matrix which in turn, increases the accuracy of subspace-based methods. Several numerical examples are presented to compare the performance of the proposed method with the relevant state-of-the-art methods. As a subspace method, simulation results demonstrate the efficiency of the proposed method in terms of estimation accuracy and computational load.
翻译:合成孔径雷达(SAR)断层成像仪(TomoSAR)是一个吸引城市基础设施高度信息提取的诱人工具。由于MUSIC算法在源本地化中广泛应用,因此当多张快照(外观)可用时,它是TomoSAR的合适解决办法。传统的MUSIC算法旨在估计散射体的整体反射特征,而连续的MUSIC算法则适合于探测稀疏点相似散射体。在这个方法类别中,连续取消是通过对MUSIC电力频谱的交替补充性预测进行的。在这项工作中,提出了名为循环变换取消MUSIC(RCC-MUSIC)的新的顺序MUSIC算法,与先前的顺序法相比,其准确性更高,其成本略有增加。此外,为了提高RCC-MISIC的性能,它与最近调和矩阵估算方法称为相关性子空间。利用相关子空间相位计算法的结果,在解析变制的矩阵中,提高了次空间计算方法的准确性,因此,提出了与计算方法的精确性压方法。