Knowledge-Based Radar Target Detection And Waveform Design Algorithms

The complexity of the electromagnetic environment in modern battlefield results in the significant degradation of detection performance for the traditional adaptive radar.The knowledge-based signal processing technique is an effective approach to solve the above problem.This thesis focuses on both building and exploiting of radar environmental knowledge base and carries out the research on knowledge-based radar target detection and waveform design algorithms.The purpose is to build the radar environmental knowledge base and exploit the a priori knowledge in the base to assist and optimize the constant false alarm rate(CFAR)detection and waveform design,which improves the detection capacity in the complex electromagnetic environment from both the radar receiver and transmitter.The main contributions of this thesis are as follows:1.For the building problem of radar environmental knowledge base,a modeling approach of the structurized and parameterized radar environmental knowledge base is proposed to provide the a priori knowledge for knowledge-based radar signal processing algorithms.By grid segmentation for radar detection region and storing the parameterized environmental information,the structurized and parameterized storage of the a priori knowledge is achieved.Meanwhile,an update algorithm based on AD and CM tests is proposed to realize the effective update of the model and parameters of clutter amplitude distribution.2.Exploiting the a priori knowledge on clutter in the radar environmental knowledge base,the CFAR detection algorithm based on the a priori knowledge of the clutter scattering intensity is proposed,which exploits the experiential statistical information of the clutter scattering intensity in the base to optimize the selection of reference cells.By the analysis of the real measured data,it indicates that the performance of the CFAR detection in heterogeneous clutter environment is improved.The CFAR detection algorithm based on the historical measurements of the clutter environment is proposed,which exploits the historical measurements of the clutter environment in the base to optimize the selection of reference cells.By the analysis of the real measured data,it indicates that the performance of the CFAR detection in heterogeneous clutter environment is improved.3.Exploiting the a priori knowledge on jamming in the radar environmental knowledge base,the waveform design algorithm based on the knowledge of jamming modulation is proposed.Given the a priori information on the jamming modulation,the model of the constant modulus constrained and non-convex waveform design problem is formulated through the criterion: the minimization of both the cross-correlation energy between jamming signal and transmit signal and the aperiodic auto-correlation sidelobe energy of transmit signal.The iteration algorithm based on the majorization-minimization framework is derived to solve the problem.The effective suppression of jamming and range sidelobe is achieved.The detection performance of radar in jamming environment is improved.4.Exploiting the a priori knowledge on environment spectrum in the radar environmental knowledge base,the waveform design algorithm for radar detection and communication functions based on the knowledge of environment spectrum is proposed.Given the a priori information on the environment spectrum,the communication information is embedded through modulating radar waveform spectrum.Then the model of the constant modulus constrained,higher-order,and non-convex waveform design problem is formulated.The iteration algorithm based on the coordinate descent method is derived to solve the problem.The proposed method realizes the dual-use functions of radar detection and communication,and can be compatible with interference spectrum.The detection performance of radar in crowded spectrum environment is improved.