Research On Cognitive Radar Function Simulation And Waveform Adaptive Optimization Technology

With the increasingly complex battlefield environment,the challenges of traditional radar in modern warfare continue to increase,and the development of radar in the direction of intelligence has become the main trend of radar development.In 2006,the concept of cognitive radar provided a solution to improve the adaptability of radar to the environment.This paper begins with the research of the working mode of the cognitive radar transmitter.Firstly,using the functional simulation technology,the cognitive radar function simulation system is built,and the simulation results are used to study the working mode of the cognitive radar transmitter.At the macroscopic level,the two processing methods of the cognitive radar transmitter are: real-time waveform design and Learn how to select waveforms in the waveform library.Then focus on the realization of these two specific processing methods,respectively study the waveform adaptive design technology and the optimal waveform selection technology in the waveform adaptive optimization technology,and separately study the existing solution for the specific solution model.The results are improved and the feasibility of the improved algorithm is verified by simulation.First of all,it is the research on the cognitive radar function simulation system.Based on the traditional cognitive radar structure and the current research status of cognitive radar at home and abroad,this paper proposes a cognitive radar architecture suitable for functional simulation of this paper,and modularizes the components in the architecture.After designing and implementing each module,each module is connected to form a complete cognitive radar function simulation system.Three typical simulation scenarios are set up,and the simulation of the cognitive radar system is carried out in three typical scenarios,and the cognitive radar operation in three typical scenarios is compared.The simulation results are analyzed.Achieve a macroscopic understanding of the working mode of the cognitive radar transmitter,and lay the foundation for the research of waveform adaptive design technology and optimal waveform selection technology.Secondly,the waveform adaptive design technique and the optimal waveform selection technique in waveform adaptive optimization technology are studied separately.In the research of waveform adaptive design technology,based on the information theory method which is still in its infancy at present,the latest proposed cognitive radar waveform design model in recent years--the maximum mutual information radar waveform design under the limitation of the signal-to-noise ratio The problem of solving the model is studied.Aiming at the problem that the traditional quadratic programming algorithm for solving the model has poor globality and frequent iterations,the particle swarm optimization algorithm is combined with the sequence quadratic programming algorithm,and the model is performed by using the formed particle swarm sequence quadratic programming algorithm.Solve and compare with the traditional sequential quadratic programming algorithm,particle swarm optimization algorithm and genetic algorithm to solve the model.The simulation results are used to verify that the particle swarm quadratic programming algorithm can improve the effect of the traditional solution algorithm on the model.In the study of the optimal waveform selection technique,the task of tracking the motion on the two-dimensional plane is the task,and the shortcoming of the traditional interactive multi-model(IMM)optimal waveform selection algorithm for the waveform library is poor.The Q learning algorithm is introduced into the traditional IMM optimal waveform selection algorithm,and the Q learning algorithm is used as the waveform selection criterion to achieve the optimal waveform selection.Through the MATLAB simulation,the improved algorithm is compared with the traditional IMM optimal waveform selection algorithm and the interactive multi-model probability data association(IMMPDA)waveform selection algorithm to verify the target tracking effect,and the algorithm is proved after the introduction of the Q learning algorithm.It can make full use of the waveform resources in the waveform library and effectively improve the tracking effect of traditional algorithms on the target.