| From the current development trend of radar technology,intelligence is the focus of future radar technology development.In this context,Canadian scholar Simon Haykin officially proposed the concept of cognitive radar in 2006.Cognitive radar has a closed-loop feedback structure,which can gradually update the knowledge of the external environment based on the feedback information from the receiver to the transmitter,so as to obtain the best target detection and tracking performance.Combining cognitive theory with inverse synthetic aperture radar(ISAR)imaging technology,we can improve the imaging ability of radar in various environments according to the offline prior knowledge and online perceived environmental characteristics of both target and environment.This thesis focuses on the key issues and difficulties in waveform design,high-resolution imaging,and system resource scheduling in cognitive ISAR.In waveform design and highresolution imaging,it studies high-resolution imaging methods of chirp signals,orthogonal frequency division multiplexing-chirp signals,two-phase encoded signals,and multi-phase encoded signals.Furthermore,for the complex electromagnetic environment,the waveform optimization and high resolution imaging methods based on the maximum SNR criterion and the maximum signal-to-noise ratio criterion are studied.The resource scheduling problem model of imaging task is established according to time and energy constraints.Because it is difficult to find the analytical solution for this model,solutions based on heuristic and heuristic-genetic algorithms are studied.The related research work of the thesis will provide an important support for improving the space target detection and recognition capability of China's ground-based ISAR and for promoting the development of “intelligent” and “systematic” radar imaging technologies.The main contribution of this thesis can be summarized as the following four parts:The first part studies the ISAR imaging method of frequency-modulated(FM)signals.Firstly,the signal model of the chirp signal is established.Then,the high resolution ISAR imaging method is studied.For the target with high speed motion,the signal model is established and a compensation based on image entropy is proposed.On this basis,the signal model of the orthogonal frequency division multiplexing-chirp signal is established,and the high resolution ISAR imaging method is studied.The second part studies the ISAR imaging method of phase modulated signals.Firstly,the model of the two-phase coded signal is established.The high-resolution ISAR imaging method of the two-phase coded signal is studied.For the problem of high sidelobe of the two-phase coded signal,a sidelobe suppression method is studied based on the mismatched filter.Then,the model of the polyphase coded signal is established,and the corresponding high resolution ISAR imaging method is studied.Finally,for the target with high-speed motion,the model of the phase-encoded signals is established,and a speed compensation method based on parameterized dictionary is proposed.The third part studies the cognitive ISAR waveform optimization and high resolution imaging methods.Firstly,the method of waveform optimization based on the maximum SNR criterion is studied.Then,the method of waveform optimization based on the maximum SINR criterion is studied.Finally,a sparse imaging model of cognitive ISAR is established for the optimized waveform,and a cognitive high-resolution imaging method based on the sparse signal reconstruction theory is proposed.The fourth part studies the cognitive ISAR resource scheduling method based on high resolution imaging.Firstly,based on the resource requirements of the imaging task and the time and energy constraints in the resource scheduling,the model of cognitive ISAR resource scheduling is established.Then,Aiming at the difficulty in finding analytical solutions,resource scheduling methods based on heuristic algorithm and heuristic-genetic algorithm are proposed separately. |
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