Cognitive Behavior Modeling And Simulation Of Cognitive Radar

Cognitive radar uses the a priori information of the target and the environment to optimize the signal processing method of the receiving end and the transmitting end,to realize the “receiver-transmitter” closed-loop processing,improve the adaptability to the geographical and electromagnetic environment,and thus have higher performance This is one of the important directions of radar technology development.This article focuses on cognitive radar and cognitive behavior,mainly in the following aspects of theoretical analysis,method research,architecture design and simulation verification work:(1)Consult relevant research data of cognitive radar at home and abroad,analyze the disadvantages of traditional radar architecture,the characteristics of typical cognitive radar system architecture,on this basis,improve the cognitive radar system architecture to make it more practical value.(2)For the environmental knowledge base,by obtaining static environmental information,a hierarchical modeling method of “geographical location partitioning → prior knowledge categories → prior knowledge content” is proposed;for waveform libraries,prior knowledge is obtained offline,The hierarchical waveform parameter database modeling method of “waveform type → signal parameter and its change mode → signal parameter value range” is proposed,and the hierarchical historical waveform database modeling method of “time information—waveform parameter—radar state” is proposed,It provides more efficient,more targeted and more reasonable priori knowledge for knowledge-based cognitive radar signal processing.For the environmental knowledge base and waveform database,an intelligent scheduling method is proposed,which realizes the automation and intelligence of the closed-loop process of “acquiring dynamic prior knowledge → updating dynamic knowledge base → assisting cognitive radar signal processing”,enhancing the cognitive radar The practicability and operability of the system have improved the efficiency of cognitive radar work.(3)Using the clutter prior knowledge provided in the environmental knowledge base,the simulation realizes the CFAR detection algorithm based on clutter prior knowledge.By introducing clutter prior knowledge in the environmental knowledge base and screening reference units,the defect of the processing capacity of the traditional CFAR algorithm under different distribution of reference units is compensated.Through simulation,it is verified that the KA-CFAR(knowledge-aided-CFAR)algorithm can effectively improve the false alarm performance and detection performance in the nonuniform clutter background.(4)For the adaptive waveform optimization problem of cognitive radar,through the adaptive update method of the waveform library proposed in Chapter 3,the waveform parameter set is optimized to improve the waveform selection performance;the historical waveform database is used to construct and train the neural network,The fast waveform selection is realized,and the simulation verifies the accuracy of the neural network to realize the waveform selection system,and the operation speed is improved by two orders of magnitude,and the optimization of the waveform selection system is realized.Through the improvement of the waveform selection algorithm framework,a waveform optimization method based on criterion switching is realized.After the cognitive radar is disturbed,it switches its own waveform selection criterion.While ensuring its own tracking performance,it achieves the purpose of anti-reconnaissance,simulation verification The method can effectively reduce the recognition rate of the opponent.