Research Of Scheduling Method For Multifunctional Radar Network

Multifunctional radar network consisits of radars which has a variety of functions(e.g.,surveillance,detection,interference and so on).This new type of radar is able to surveille multiple regions,and plays an important role in modern war.To improve the performance of multifunctional radar network,the position of radars and resources need to be scheduled well.However,when time and computational resources are limited,and preferential placement is required,the original radar placement algorithms can’t handle it well and obtain a good performance.Thus,the above problems remain largely unexplored,and it’s the main topic of this thesis.To satisfy the radar placement requirements of different scenes,the deployment and resources scheduling of multifunctional radar network based on improved Particle Swarm Optimization(PSO)algorithm has been studied.The main contents are as followed:1.Aiming at the deployment of multifunctional radar network,we analyse the radar netting principles,and the optimization problems for radar scheduling is introduced.Then,the deployment algorithm for multifunctional network radars based on PSO is proposed.The simulation confirmed that the deployment performance generated by above algorithm is better than the performance generated by random radar deployment.2.To obtain the optimal placement result with limited time and computational resource,a fast radars deployment algorithm using convergence criterion based on interval distance is studied.First,the interval distance is introduced.Then,to stop the algorithm adaptively,an iteration convergence criterion is proposed.Finally,comparing with traditional radar deployment algorithms,simulations proves that the proposed algorithm can achieve optimal placement results using time and computational resource as less as possible.3.Considering different regions taking on different degrees of importance,a radar placement algorithm based on PSO integrating with preference is studied.We introduce the method of choosing the best particle by estimating distance from reference point.By comparing with the performance by using PSO without preference,simulations verify that the deployment algorithm can improve the performance of preferential regions and provide more choices of deployment methods.4.Considering that the reference points are hard to ascertain and iteration stopping criterion remains unknown in traditional radar deployment algorithm with preference,a fast radars deployment algorithm integrating with preference is studied.First,the method of ascertaining reference point under the different reference directions is studied.Then,adaptive algorithm stopping criterion integrating with preference is introduced.Finally,under the different reference directions,the simulations prove that the preferential region performance is improved with less time and computational resources.