## 4 Discussion  
Discussion of the Traditional Compressive Sensing Method and the Multi-Look Based Compressive Sensing Layover Separation Method  
The simulation experiments in Section 3.1.1 show that results of height evaluation accuracy by the traditional compressive sensing method have an average height error of 7 m, and are obviously worse than the multi-look compressive sensing method, with an average height error of 1 m below. Although the results of the two methods have outliers, we can determine the number of scattering points and find the maximum peak value in descending order to avoid outliers and ensure the stability of scattering point estimation. According to the number of scattering points, the height evaluation accuracy obtained by traditional compressive sensing behaves poorly with small number of baselines (four), which is consistent with the baseline requirements in [18]. The poor results of traditional compressive sensing are due to the loss of sparsity of data stacks obtained with four baselines and can be considered not to be met with essential conditions of compressive sensing. In essence, due to the small number of observation vectors, the traditional method has completely failed to meet the sparse characteristics of compressed sensing, so the performance is relatively poor and unstable. Meanwhile, the multi-look compressive sensing method behaves better than traditional compressive sensing because  
Figure 9: Height distribution of scattering points. (**a**) Multi-look compressive sensing method; (**b**) Separable approximate sparse reconstruction algorithm. The horizontal axes represent pixels from 600 to 800 in the slant range, and the vertical axes are heights in the elevation direction with the unit m.  
of the increased sparsity of compressive sensing provided by the multi-look mode, and thus the proposed method has a stable layover separation ability and has high efficiency in its height estimation ability of scattering points in layover areas. The measured data experiment is shown in Section 3.1.2. The baseline of this system is smaller than MEMPHIS, which can improve the difficulty of layover separation processing which is consistent with the results in Figure 5a. The results of traditional compressive sensing in Figure 5a have a very high sidelobe and a lot of singular values, have already lost sparsity in the compressive sensing domain, and the data have already become continuous, which greatly reduces the stability of scattering point estimation, while the results of real compressive sensing are discrete and sparse. In this condition, the layover separation can only be processed by determining the number of scattering points and finding the maximum peak value in descending order to obtain the height of scatterers which is less reliable in the compressive sensing domain. Furthermore, the height estimates of adjacent pixels in layover areas jump up and down dramatically in Figure 6a, making them discontinuous enough and completely inconsistent with the actual scenario. Thus, the poor continuity of height evaluation by the traditional compressive sensing method shows that the traditional method is not suitable for layover separation. However, the multi-look compressive sensing method provides sparsity generally in Figure 5b and has no sidelobes and fewer outliers. In addition, the height estimates of adjacent pixels in layover areas have fine continuity in Figure 6b, and is consistent with the actual scenario. Additionally, the height difference of two scatterers in layover areas is lower than 50 m, which is not restricted by the theoretical resolution of the height direction. Thus, this method can break the limits of theoretical resolution and has a high resolution of scatterers in layover areas. The look number increases the observation vectors, which can be considered as increasing the data from antennas. In addition, the separation of scatterers and height continuity behaves better than the traditional compressive sensing method in Figure 6a,b.