Methods And Applications Of Compressed Sensing Based Isar Imaging For Space Target

In the field of ISAR imaging for space target, the traditional range- doppler(RD)imaging algorithm and signal acquisition method always require large amount of raw data to obtain high-resolution images. This dissertation focuses on the ISAR imaging for space target based on compressed sensing(CS), aiming to solve the theoretical problems in CS imaging and errors caused by imperfect measurement in actual radar application. The study in this dissertation combines the emerging trend in modern radar, such as digital radar receiver and Phased Array Radars(PAR). Great effort is made to solve some practical problems and to make some breakthrough in ISAR imaging for space target. The main contents of this dissertation include CS digital radar receiver based on random sampling, 2D radar image reconstruction, auto-focusing problem in CS imaging with limited pulses, CS multi-target imaging using PAR, and reducing the micro-Doppler effect in CS imaging for target with rotating parts.Chapter 1 makes a brief introduction about the background and significance of the research. The history and current status of the research on space target ISAR imaging is firstly illustrated. A detailed review on the development of CS theory and its application in radar, especially in ISAR imaging, is made in this chapter. The confronting challenges and existing technical problems in the field of CS ISAR imaging are analyzed and summarized afterwards, which proves the importance of the research in this dissertation. The main content of the whole thesis is presented in the last section.Chapter 2 proposes a novel design of intermediate frequency(IF) digital receiver for wideband ISAR radar based on(CS). The random sampling technique is utilized for the convenience in engineering application, which makes it possible to digitize the wideband IF signal using a commercial analog-to-digital converter(ADC). First, a novel basis for the sparse representation of real-valued ISAR radar echoes is built in this chapter, and an orthogonal CS reconstructing algorithm is proposed based on this. Using our proposed method, the complex-valued range profile of target can be directly reconstructed from the sub-sampled real raw echo. The phase information of target range profile, which is very important for the coherent processing in ISAR imaging, is well reserved during the reconstruction. Besides, a novel 2D CS measuring strategy for ISAR imaging radar is proposed in this chapter. It is proved that sub-sampling can be made in both range and azimuth dimensions of radar echoes. In order to handle 2D reconstructing problem, the2D-SL0 algorithm is referred to reconstruct the imaging by 2D reconstruction, which can reduce the computational complexity and memory usage significantly. Moreover, the results of simulation and actual radar signal processing demonstrate the feasibility and superiority of the proposed methods.In the third chapter, the auto-focusing problem of CS ISAR imaging with limited pulses is studied. Due to the inadequate pulses compared to traditional RD imaging, conventional phase compensating algorithms can’t work in CS imaging. In this chapter,an iterative algorithm is proposed to compensate the phase errors and reconstruct highresolution focused image from limited pulses. In each iteration, the image of target is reconstructed by CS method, and then the estimation of phase errors is updated based on the reconstructed image. By cycling these steps, well-focused image can be obtained. The smoothed ?0(SL0) algorithm is used to reconstruct the image, and the idea of minimum entropy optimization is used to estimate the phase errors. Besides, a method of extracting range bins in range profile based on amplitude information is proposed, which can reduce the computational complexity and improve the speed of convergence considerably. Both simulation and experiment results from real radar data demonstrate the effectiveness and feasibility of our method..Chapter 4 focuses on the research on multi-target simultaneous ISAR imaging. It requires the radar keeps tracking the target during coherent processing intervals(CPI).This limits the radar’s multi-target imaging ability, especially when those targets appear simultaneously in different observing scenes. To solve this problem, this paper proposes a multi-target ISAR imaging method for PAR based on CS. This method explores and exploits the agility of PAR without changing its architecture. Firstly, the transmitted pulses are allocated randomly to different targets, and the image of each target can be then reconstructed from limited echoes using CS algorithm. A pulse allocation scheme is proposed based on the analysis of target size and rotation velocity, which can guarantee that every target get enough pulses for effective CS imaging. Self-adaptive mechanism is utilized to improve the robustness of the pulse allocation method. Simulation results are presented to demonstrate the validity and feasibility of the proposed approach.Chapter 5 studies how to reduce the micro-Doppler effect in compressed sensing ISAR imaging for aircraft using limited pulses. In CS ISAR imaging for space target, the image of target can be reconstructed using fewer pulses with random pulse repetition intervals than conventional RD method. However, the micro-Doppler(m D) effect induced by the non-stationary parts of aircrafts still causes defocusing as in RD imaging. A method to reduce the m D effect in CS ISAR imaging in this chapter. The CS based short-time Fourier transform is deployed to reconstruct the time-frequency(TF) spectrogram of echoes. A L-statistics based algorithm is applied to separate the non-stationary scatters from rigid main body in TF domain. Furthermore, CS algorithm is used to reconstruct the cross-range image of main body after m D separation. Compared with direct CS imaging without m D removal, better image can be obtained. The results of both simulated and real data processing demonstrate the validity of the method proposed in the end.A summary of the key points of research and innovation in this dissertation is illustrated in Chapter 6, which is followed by some introduction of the possible next researching direction in future work.