## 3 Problem Statements and _A Priori_ Knowledge Integration
### Integration of A Priori Knowledge  
Except for some military applications of GMTI, a great deal of targets may travel strictly on the road with a specific moving direction and velocity range. These types of prediction maybe interpreted as a priori knowledge that can be acquired, analyzed, and stored independent of image acquisition. In this paper, road network databases serve as a basic source for acquiring a priori knowledge. These databases contain road axes in the form of polygons and attributes, such as road width, maximum velocity, and road direction. [PERSON] et al. [16; 17; 18] proposed a method to transform the geographical coordinates of each road point to corresponding azimuth beam center coordinates in the airborne radar range/azimuth plane. The azimuth beam center position of the detected moving vehicle is then directly given by the intersection of the vehicle signal with the mapped road point. The road network stored in the databases may represent all possible moving target positions in a real scene. In this paper, based on the road network database and SAR images, the displacement map and moving target interferometric phase map around the roads can be computed pixel by pixel [11; 12; 13; 14; 22; 23]. In the single SAR transmits and several SAR receive modes, the moving target interferometric phase \(\psi\) can be expressed as [11; 12; 13; 14; 22; 23],  
\[\psi=\frac{2\pi v_{r}B_{1,2}}{\lambda v_{s}} \tag{12}\]  
where \(v_{r}\) is the target ground radial velocity and \(v_{s}\) is the satellite velocity. The azimuth displacement \(\Delta a\) is defined as [11; 12; 13; 14; 22; 23],  
\[\Delta a=a_{t}-a_{road} \tag{13}\]  
where \(a_{t}\) is the moving target azimuth position in the SAR image and \(a_{road}\) is the corresponding road segment position. The azimuth displacement is also caused by the target's across-track motion, which occurred due to SAR focusing. \(\Delta a\) is always exploited to compute the targets' interferometric phase and across-track velocities [11; 12; 13; 14; 22; 23]. \[\Delta a=\frac{2v_{r}v_{g}}{\lambda f_{r}}=\frac{\psi v_{g}v_{s}}{\pi f_{r}B_{1,2}} \tag{14}\]  
where \(f_{r}\) is the Doppler frequency modulation rate and \(v_{g}\) is the radar beam velocity on the ground. For the master satellite SAR, which both transmits and receives the radar signal, \(f_{r}\) can be computed as,  
\[f_{r}=-2v_{g}^{2}/\lambda r_{t} \tag{15}\]  
\[\Delta a=-\frac{v_{r}r_{t}}{v_{g}}=-\frac{r_{t}\psi\lambda}{4\pi B_{12}} \tag{16}\]  
Firstly, based on the road network database and radar parameters, the displacement measurements of every pixel around its corresponding road segments in the SAR images, which is called as displacement map, can be computed according to Equation (13). Secondly, the moving target's interferometric phase map can be computed directly using the displacement map according to Equation (14). Finally, the moving target steering vectors estimation can be computed using Equation (2). In Equation (6), the searched moving target steering vectors can be directly replaced by estimation results from the interferometric phase map, which can reduce the steering vector mismatch effectively. The expected moving target steering vectors, which are derived from the interferometric phase map, can be directly integrated to the adaptive clutter cancellation processor. The estimation accuracy has been analyzed in the paper [11; 12; 13; 14; 22]. In Section 4, the expected steering vectors can also be directly integrated into the two kinds of adaptive detection techniques, in which the searched steering vectors are also replaced by the estimation results derived from the interferometric phase map. Compared with the traditional adaptive clutter cancellation technique according to Equations (1)-(4), the proposed a priori knowledge-based adaptive clutter cancellation technique replaced the moving target steering vector searched results with the estimated results.