## 4 Discussion
Furthermore, the increased observation vectors expand the dimension of SVD evaluation, make the condition of compressive sensing more sufficient, and the noise level obtained by projection to the observation vector can be more robust. Thus, the stability of sparsity solution in multi-look compressive sensing is more stable. Based on the above analysis, in order to solve the layover problem of airborne multi-baseline SAR system, the traditional L1-norm compressive sensing method deteriorates the reliability of the L1 norm solution due to the small number of baselines, which makes the sparse reconstruction ability, the separation ability of scatterers in layover areas and the height stability of scatterers poor. To solve this problem, this paper adopts multi-look idea to the traditional method of L1 norm compression perception, to increase the dimension of the measured data, reduce the noise level, and improve the sparsity of processing data, thus the solving of the L1 norm is nearly L0 norm solution. In other words, RIP characteristics and incoherence in compressed sensing are improved, and the multi-look processing mode improves the sparse reconstruction ability, the scatterer separation ability in layover areas, and makes the scatterer height direction estimation more stable and reliable. Simulation and measured data verify the effectiveness of the method. Discussion between Multi-Look Based Compressive Sensing Layover Separation and Separable Approximate Sparse Reconstruction Method  
The simulation experiments in Section 3.2.1 show that results of height evaluation accuracy by separable approximate sparse reconstruction method have an average height error of 1 m below, and are comparable than multi-look compressive sensing method. Though the results of separable approximate sparse reconstruction have main lobes, we can determine the number of scattering points and find the maximum peak value in descending order to ensure the stability of scattering point estimation. Moreover, the results of separable approximate sparse reconstruction method are stable enough. In essence, the multi-look separable approximate sparse reconstruction method transforms the L1-L2 norm optimization into iterative soft threshold shrinkage processing mode,thus, this method keeps the scatterer separation ability and height evaluation ability of multi-look compressive sensing method in layover areas and run the processing in iteration mode. Compared with their processing time, the processing time of separable approximate sparse reconstruction method is nearly one quarter of processing times of multi-look compressive sensing method and comparable to the operation time of traditional compressive sensing method (0.62 s). Thus, layover separation method by separable approximate sparse reconstruction can reduce the computation time while keeping the stability of layover separation. The measured data experiment is shown in Section 3.2.2. The results of separable approximate sparse reconstruction method from Figure 8b have certain main lobe width which may have little influence on scatterer separation in layover areas if peak of the main lobe in two scatterers are separable enough. The results of multi-look compressive sensing in Figure 8a demonstrate a little more sparsity than those of the separable approximate sparse reconstruction method in Figure 8b. Meanwhile, the separable approximate sparse reconstruction method run the processing by determining the number of scattering points and finding the maximum peak value in descending order, and the influence of stability of scattering point estimation is smaller than traditional compressive sensing method, which is absolutely dependent on the sparsity. Furthermore, the height estimates of adjacent pixels in layover areas jump up and down dramatically in Figure 9b, are continuous and consistent with the actual scenario, making them comparable with the results for the multi-look compressive sensing method.