Inverse Synthetic Aperture Radar Imaging Technique Based On Compressed Sensing

High resolution inverse synthetic aperture radar (ISAR) imaging technique is of greatsignificance to radar target recognition and feature extraction. Taking full advantage ofthe sparsity of radar target reflectivity and the direct information sampling property ofcompressed sensing (CS), this dissertation focuses on ISAR imaging technique based onCS, serving for overcoming the inherent limitations of traditional ISAR imagingsystems. The main research efforts include the high resolution imaging techniques ofrotary platform targets, high-speed targets and targets with complex motion.Chapter1illustrates the background and significance of this research subject, andintroduces the present status of high resolution imaging radar and ISAR imagingtechnique. Then the development and applications of CS theory, particularly in radarimaging domain, are reviewed and summarized, after which the main content of thisthesis is presented briefly.Chapter2introduces the basic principle of CS, and makes a careful analysis of themathematic model and essentials of CS firstly. Then, the sparsity mechanism of radarechoed signal is analyzed based on the scattering center theory in high frequencydomain and radar imaging principle. Finally, an imaging method based on CS byrandom convolution is studied from a practical perspective, which is convenient torealize and applicable to various radar waveforms.Chapter3focuses on the CS-based imaging technique of rotary platform targets andfast reconstruction algorithms. Firstly, to alleviate the direct sampling pressure ofwideband linear frequency modulated (LFM) radar, a sparse dictionary constructionmethod based on stretch processing and FFT operation is presented, following which anovel compressive radar imaging method is proposed, and it can remarkably reduce thedata rate of radar imaging system while maintaining imaging quality. Then, in view ofthe low data usage factor of stepped frequency radar, two imaging methods, termedCS-based2D joint imaging algorithm and CS-based2D decoupled imaging algorithm,are proposed. Both can get clear ISAR image with less data samples, and simplify thehardware design of radar system because the coherent mixing operation is incorporatedinto the sparse dictionary. Afterwards, considering the high computation complexity ofCS imaging methods, in light of the2D separability of sparse dictionary andcompressive measurement, several fast algorithms for radar image formation are studied,and an improved OMP algorithm is proposed, which possesses prominent superiorityover conventional CS algorithms in terms of storage and computation.Chapter4researches into the CS-based imaging technique of high-speed targets. ForLFM radar, according to the sparsity of the dechirped high-speed target echo infractional Fourier domain, Analog-to-Information Conversion (AIC) is recommended totake compressive measurements, following which the radar image can be recovered via nonlinear optimization, and the efficient golden section method is adopted to find theoptimal transform order taking the sparsity of range profile as search criteria. Theproposed method can achieve high resolution imaging of high-speed targets withoutadditional velocity compensation, but with less data samples and higher imaging quality.In allusion to the sensitivity to Doppler and low data usage factor of stepped frequencyradar, making use of the predesigned pulse repetition interval (PRI) and/or the phasecancellation principle, a compressive imaging method based on random steppedfrequency waveform design is put forward, which can effectively reduce the data ratewhile overcoming the negative influence of Doppler effect. Similarly, as for linearlymodulated stepped frequency (LMSF) radar, to overcome the sensitivity of inter-pulsecompression to Doppler and reduce the total data rate, a compressive imaging methodbased on random LMSF waveform design is proposed. Simulation experiments indicatethat the proposed CS-based methods can get high quality ISAR images of targets withhigh-speed motion.Chapter5studies the CS-based imaging technique of targets with complex motion.First, in light of the sparsity of the range cell echo of radar targets with non-uniformrotation in matching Fourier domain, an imaging method based on CS is proposed forboth sparse aperture and short aperture cases, and the relative rotation parameter isestimated by the optimal search in fractional Fourier domain for the sparse dictionaryconstruction. The proposed method resolves the contradiction between limited pulsedata and high azimuth resolution, and performs better than the existing methods. Second,for rapidly spinning targets, a2D imaging method using the orbital information ispresented, where CS is incorporated for reducing the requisite pulses. Then, thespinning information is utilized for2D slice imaging via the complex-valuedback-projection transform or CS method, consequently the high resolution3D image isobtained. The method proposed here reduces the complexity of3D imaging effectivelythrough pre-estimating the altitude information of scattering centers. Third, CS conceptis introduced into the ISAR imaging problem of precession targets with rotationallysymmetrical structure, and high resolution imaging can be achieved with a smallnumber of measurements. The proposed method performs better than existing methodsin terms of imaging quality and robustness to precession parameter.Chapter6summarizes the research work and main innovations, and points out thefuture work to be researched.