With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground moving target indication (GMTI) radar systems has received great interest. In GMTI, moving target relocation is an essential requirement, because the positions of the moving targets are usually displaced. For a multichannel radar system, the position of moving targets can be accurately obtained by estimating their interferometric phase. However, the high position accuracy requirements of antennas and the computational resource requirements of algorithms limit the applications of relocation algorithms in UAV-borne GMTI radar systems. In addition, the clutter’s interferometric phase can be severely affected by the undesired phase error in the site. To overcome these issues, we propose an improved knowledge-based (KB) algorithm. In the algorithm, moving targets can be relocated by comparing their interferometric phase with the clutter’s phase. As for the undesired phase error, the algorithm first employs a random sample consensus (RANSAC) algorithm to iteratively filter the outliers. Compared with other classic relocation algorithms, the proposed algorithm shows better relocation accuracy and can be applied in real-time applications. The performance of the proposed improved KB algorithm was evaluated using both simulated and real experimental data.

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground-moving target indication (GMTI) radar systems has received great interest. However, because of size, weight, and power (SWaP) limitations, the UAV may not be able to equip a highly accurate inertial navigation system (INS), which leads to reduced accuracy in the moving target relocation. To solve this issue, we propose using an image registration algorithm, which matches a Doppler beam sharpening (DBS) image of detected moving targets to a synthetic aperture radar (SAR) image containing coordinate information. However, when using conventional SAR image registration algorithms such as the SAR scale-invariant feature transform (SIFT) algorithm, additional difficulties arise. To overcome these difficulties, we developed a new image-matching algorithm, which first estimates the errors of the UAV platform to compensate for geometric distortions in the DBS image. In addition, to showcase the relocation improvement achieved with the new algorithm, we compared it with the affine transformation and second-order polynomial algorithms. The findings of simulated and real-world experiments demonstrate that our proposed image transformation method offers better moving target relocation results under low-accuracy INS conditions.