## 6 Discussion  
For military use, spaceborne SAR-GMTI systems can detect ground moving vehicles and estimate their parameters. For civilian use, spaceborne SAR-GMTI systems can monitor the traffic situation in urban and motorway areas. For traffic monitoring use, a ground vehicle may move strictly on the road with a maximum or minimum velocity limitation and velocity direction limitation. For SAR-GMTI processing, the ground moving vehicles may be detected and relocated to a certain road. The road network information can be exploited as a priori knowledge to improve the SAR-GMTI signal processing performance.  
The proposed method has many advantages. Firstly, most of the existing SAR-GMTI processing methods assume that no prior information can be used, which is appropriate for military use. For civilian applications, especially for traffic monitoring use, the off-road moving targets can be ignored based on the a priori known road network. This  
Figure 7: The SAR-GMTI signal processing results for AMF and the proposed method. (**a**) real positions of the eighteen simulated moving targets (red dots); (**b**) traditional AMF technique detection results (red dots represent the accurate detection results, green dots represent the false alarms); (**c**) detection results of the proposed method (red dots represent the accurate detection results, green dots represent the false alarms); (**d**) relocation results of the proposed method (red dots represent the accurate detection and relocation results, green dots represent the false alarms, blue dots represent the accurate detection and false relocation results).  
operation in the SAR-GMTI processing may reduce the signal processing complexity and computational load.  
Secondly, the Pd versus \(\cos^{2}(\theta)\) is shown in Figures 1 and 2 for AMF and ABORT. Pd and Pc versus \(SCNR_{in}\) of the proposed multiple hypothesis testing technique is shown in Figure 4, which is based on ABORT and AMF. For the large mismatch angle \(\theta\), the Pd may decrease quickly and approximate to zero. Based on the priori knowledge of the road network database, the moving target steering vectors may be estimated accurately. As shown in Figure 4, even under low input SCNR, the Pdis still high enough to detect the presence of a target. The proposed technique eliminated the influence of the steering vector mismatch for the detection performance of the adaptive clutter suppression technique.  
Thirdly, the integration of a priori knowledge into SAR-GMTI processing may improve the moving vehicles parameter estimation accuracy. Due to the relation of the along-track interferometric phase and the target velocity in the across-track direction, the interferometric phase is always exploited to estimate the moving target velocity parameters and relocate the moving target to its real position. However, as shown in Figure 7, the traditional AMF technique may not relocate the detected targets because the target's interferometric phase estimation may be wrapped within \([-pi,+pi]\) for large radial velocities. The interferometric phase may also be influenced by clutter and noise. The proposed method performs velocity estimation based on the along-track displacement of detected vehicles from their corresponding roads. The multiple hypothesis testing technique and the azimuth displacement map are exploited to resolve the potential ambiguities. The incorporated road data not only improves the estimation accuracy but also limits the search space. As shown in Figure 7, based on the priori knowledge of road network information, the proposed method may detect the moving targets effectively and relocate them to the real positions with a small number of relocation errors because of the corresponding road classification errors.