difference between two echo signals corresponding to the same target, and extracts the three dimensional (3D) information of the measured area combined with the InSAR geometric model [1; 2]. InSAR technology is not only used in 3D Earth mapping, but also widely used in moving target detection, surface change detection, glacier movement detection, natural disaster prediction (earthquake, volcano, landslide and other disaster monitoring), land resource classification, the military and other fields [3; 4]. However, InSAR does not have the ability to resolve the height direction, nor can it separate cases in which the same resolution unit contains multiple scatterers [5]. Meanwhile, due to the side-looking mode of InSAR, problems such as the layover and shadow phenomena inevitably occur in the SAR image acquired. Additionally, with the development of high-resolution SAR and the demand for large-scale surveying and mapping, the problems of the layover and shadow phenomena cannot be ignored [6; 7]. Among them, the shadow problem can be addressed by means of multi-angle flight data processing, three-dimensional circular flight data processing, and other external data, etc. In the case of layover problems, most of the SAR images of the same ground object from different perspectives are acquired by means of multi-flight, and then the scattering points in layover areas can be separated according to their coherence and high precision processing algorithm. Research on the layover problem has also become one of the hot spots and difficulties for experts from various countries in recent years [8; 9]. Various methods have been applied to deal with the layover problem, such as spectral estimation methods, maximum likelihood estimation methods and compressed sensing methods. Among them, spectral estimation methods such as Capon, MUSIC, ESPRIT and other methods have the capability of providing hyper-resolution in the height direction, which generate a height-normalized spectral function through power spectrum estimation, and realize the separation of scattering points in layover areas and the estimation of the height by using the maximum search method [10; 11; 12]. The premise of this kind of method is to estimate the number of scattering points in layover areas. Then, the height of the scattering point corresponding to the spectral peak is obtained according to the criterion of amplitude descending order. At the same time, the separation ability of scattering points and the resolution of height direction are limited by Nyquist's sampling law, which dictates that the resolution of the height direction is limited by the number of antennas and the length of maximum baseline [10; 13]. Maximum likelihood estimation methods adopt multi-antenna SAR data to construct a probability density function, and then calculate the maximum by means of multidimensional search probability density function, and obtain the corresponding dimensions of height [14; 15]. In theory, these methods are not limited by the law of Nyquist sampling, although due to the exhaustive search method used to search for the maximum, the operation time of this method is too long, especially when the number of scattering points in a resolution element is greater than three. In addition to the existing spectral estimation layover separation methods and Maximum likelihood estimation methods, compressive sensing layover separation methods are not limited by the Nyquist sampling theorem, and have the advantages of high resolution and good reconstruction performance, becoming one of the hot spots of tomography SAR (TomoSAR) technology research in recent years [16; 17]. Compressed sensing methods transform the L0 norm NP-hard problem into the L1 norm problem by using the compressed sensing theory. Thus, the sparse solution is obtained by means of optimization, and then the corresponding scattering point separation and height estimation are obtained.