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.

Polyethyleneimine-modified calcium carbonate nanoparticles for p53 gene delivery

In this study,calcium carbonate(CaCO3)nanoparticles with spherical structure were regulated by arginine and successfully synthesized via a facile co-precipitation method.The average particle size of as-prepared CaCO3 was about 900 nm.The properties of nanostructured CaCO3 particles were characterized by scanning electron microscope,Fourier transform infrared spectroscopy,Xray diffraction and size distribution.After modified with polyethyleneimine(PEI),the ability of PEICaCO3 nanoparticles to carry GFP-marked p53 gene(pEGFP-C1-p53)into cancer cells to express P53 protein were studied.Meanwhile,the cytotoxicity,transfection efficiency,cells growth inhibition and the ability to induce apoptosis by expressed P53 protein were conducted to evaluate the performances of PEI-CaCO3 nanoparticles.The results show that prepared PEI-CaCO3 nanoparticles had good biocompatibility and low cytotoxicity in a certain concentration range.PEI-CaCO3 effectively transfected pEGFP-C1 gene into epithelial-like cancer cells.And with the expression of GFP-P53 fusion protein,pEGFP-C1-p53-gene-loaded PEI-CaCO3 particles significantly reduced the proliferation of cancer cells.These findings indicate that our PEI-modified CaCO3 nanoparticles are potential to be successfully used as carriers for gene therapy.