With the rapid development of unmanned aerial vehicle (UAV), techniques are required for the detection and surveillance of such UAVs for both civil and military purposes. This paper addresses the maneuvering target detection with an airborne radar system based on a space-time adaptive processing (STAP) technique. A general signal model is established for the moving target, where the target is parameterized with angle, velocity, acceleration, and backscattering coefficient. A general adaptive matched filtering method is proposed involving multiple-dimensional search. An enhanced approach is devised by using a discrete searching technique within the acceleration dimension in order to reduce the computational complexity. The simulation examples are provided to demonstrate the effectiveness of the proposed method.INDEX TERMS Airborne radar system, space-time adaptive processing, unmanned aerial vehicle, multipledimensional search, discrete searching technique, discrete step size.

For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condition. It is because of the realistic heterogeneous environment faced by airborne radar. Moreover, one should eliminate contaminated training samples before CCM estimation. Aiming at the problems of the computational complexity and susceptibility to the outlier of the traditional generalized inner product (GIP) method, a clutter subspace-based training sampling selecting method is proposed combined with specific distribution in the space-time plane of clutter spectrum. Theoretical analysis and simulation results verified the proposed method and indicate that the proposed method is easy to construct CCM and has lower computational complexity and sensitivity to outliers.

The real-time closed-loop simulator of Synthetic Aperture Radar (SAR) needs to input the SAR radar reference map as the simulation target scene information in the process of simulation. However, it is difficult to obtain the scene map of SAR radar in the detection range that is realistic and consistent with the task target, and the data sets are limited. In this article, Cycle-Consistent Adversarial Networks (CycleGAN) is applied to realize the migration learning of SAR radar reference map and solve the task required of a directional generation of SAR images.

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When synthetic aperture radar (SAR) is conducting remote sensing or terrain mapping, its radar beam is inevitably occluded by the variations in the under-test topography. Although back-projection algorithm (BPA) can theoretically directly solve the imaging problems of topography variations that most current SAR imaging algorithms cannot handle, these BPAs only solve the phase focusing of SAR echo signal, and do not consider the mismatch of SAR imaging results caused by topography occlusion. To solve the mis-imaging issue of the occluded area generated by BPA under the case of topography variation, a topography-based BPA (Topo-BPA) is proposed in this paper. Firstly, a new beam occlusion judgment algorithm based on spherical wave assumption is proposed, and its core is depression angle interpolation and depression angle updating. Then, the proposed Topo-BPA embeds the proposed beam occlusion judgment algorithm before the classical BPA, which not only did not reduce the focus depth of BPA, but improved the imaging accuracy of classical BPA. Finally, numerical experiments have demonstrated the superiority of the Topo-BPA’s performance in comparison with classical BPA.