Study Of ISAR Imaging For Space Targets

For several decades of development, synthetic aperture radar and inverse synthetic aperture radar (SAR/ISAR) imaging theories and algorithms have been perfect gradually. However, with further upswing of global space resource development and intensification of future space battle trend, spatial targets become more important. New problems and challenges in ISAR imaging of spatial targets occur. In order to fully exert signal processing influence and improve spatial target imaging capability of the radar, for some aspects of ISAR imaging, measures such as conceptual analysis, theoretical derivation and data demonstration are studied in this dissertation. The main work of this dissertation is as follows:[1] Chapter 1 is the introduction. It reviews ISAR development and introduces the dissertation's research background and main work.[2] Chapter 2 overviews conventional ISAR imaging principle and various technological methods used for ISAR imaging. Based on this, for characteristics of spatial targets in high speed, it discusses an effective satellite imaging method.[3] Chapter 3 studies sparse band imaging of spatial targets. Common radar signal waveform is used for analysis. Difference between different band signals observed for the same target is discussed in detail. Corresponding compensation method is given to make different band signals have the same all-pole signal model so as to obtain equivalent ultra-wide band ISAR with effective interpolation. For practical problems omitted previously in sparse band imaging, the above study obtains effective solution by analysis and establishes theoretic basis of future multi-band radar imaging.[4] Chapter 4 presents a sparse aperture imaging algorithm. Under the condition of sparse apertures, the interval of sub-apertures is very big. How to effectively use various echo data to predict interpolation for aperture loss part is the focal point of Chapter 4. Firstly, an effective sparse aperture data translation motion compensation method is proposed according to characteristics of sparse apertures; then, a sparse aperture interpolation prediction method is proposed; finally, simulations and measured data show the effectiveness of the proposed methods.[5] Chapter 5 studies narrow band imaging of spatial debris. At present, ISAR band width still does not satisfy precision imaging requirements of spatial debris. In this chapter, based on the study of single-range Doppler interference (SRDI), according to spinning characteristics of spatial debris, a narrow band imaging algorithm of spatial debris is proposed. The proposed algorithm combines match filtering with CLEAN technology to image spatial debris. By analysis and simulation demonstration, compared with previous SRDI imaging algorithms of spatial debris, the proposed algorithm has the advantages of higher resolution and less computational burden and can be applied to practice more effectively.[6] High spinning is a main motion attitude of spatial targets. For imaging of this kind of targets, good images can't be obtained by common ISAR technology. Chapter 6 presents a 3D imaging method based on match filtering for targets in high spinning. The method uses match filtering to successively obtain slices along spinning axis, and obtain 3D image by synthesizing the slices.[7] Chapter 7 presents a GRT-CLEAN 3D imaging method for targets in high spinning. The proposed method uses range-slow time data, introducing curve detection method for image processing (GRT) to estimate spatial position parameters of each scatterer, and uses CLEAN technology to improve parameter estimation accuracy. Different from common ISAR imaging methods, the methods presented in Chapter 6 and Chapter 7 fully use spinning characteristics of the targets and image according to scatterer phase-range change rule, providing two new thoughts for future radar imaging.[8] Chapter 8 is the summary of the dissertation. It also discusses future research areas to be further studied.