New UWB imaging algorithm for multiple targets detection

Because the conventional ultra wideband（UWB） radar imaging algorithm cannot meet the demand in the capability of multiple targets detection,a novel UWB radar imaging algorithm based on the near field radiation theory of dipole is presented.On the foundation of researching the principle of a time domain imaging algorithm,the back projection（BP） algorithm is derived and analyzed.Firstly,the far field sampling data are transferred to the near field sampling data by using the near field radiation theory of dipole.Then the BP algorithm is applied to target detection.The capability of the new algorithm to detect the multi-target is verified by using the finite-difference time-domain method,and the threedimensional images of targets are obtained.The coupling effect between targets for imaging is analyzed.The simulation results show that the new UWB radar imaging algorithm based on the near field radiation theory of dipole could weaken the coupling effect for imaging,and as a result the quality of imaging is improved.

Staring-imaging satellite pointing estimation based on sequential ISAR images

Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and surveillance.However,feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation methods.Especially,the emergence of staring imaging satellites,characterized by complex kinematic behaviors,presents a novel challenge to this task.To address these issues,this article proposes a pointing estimation method based on Convolutional Neural Networks(CNNs)and a numerical optimization algorithm.A satellite’s main axis,which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net(SARRN),is chosen for investigation in this article due to its unique structure.Specifically,considering the kinematic characteristic of the staring satellite,an ISAR imaging model is established to bridge the target pointing and the extracted axes.Based on the imaging model,pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem,and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this problem.Finally,the pointing of targets and the cross-range scaling factors of ISAR images are obtained.Simulation experiments based on actual satellite orbital parameters verify the effectiveness of the proposed method.This work can improve the performance of satellite reconnaissance warning,while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.