| The radar networking system has the capabilities of anti-stealth target,anti-radiation missile,anti-integrated electronic interference and anti-ultra-low altitude penetration,which can significantly improve the detection probability and survival ability of the radar in the network in modern electronic countermeasures.The problem of radar network optimization,namely the deployment of radar station,is one of the important factors affecting the performance of networked radar,and is also the key technology of radar network.At present,the radar optimization networking problem mainly aims at the comprehensive performance of the radar networking system,puts forward the deployment principle and quantifies it,establishes the mathematical model according to the quantized index,and obtains the networking scheme by solving the mathematical model.However,radar optimization networking is a nonlinear optimization problem with constraints,and the solution space is very complex.It is difficult to find the optimal solution by using traditional mathematical analysis methods.Therefore,aiming at the radar optimization networking problem in multiple scenarios,this paper adopts the improved intelligent optimization algorithm to study the radar networking method.The details are as follows:(1)Aiming at the problem of radar optimization network based on airspace coverage coefficient,a radar optimization network method based on improved northern Goshawk optimization algorithm is proposed.Firstly,the optimization algorithm of the northern goshawk was improved.The improved algorithm used cubic chaotic mapping to initialize the population,the optimal individual guided position updating strategy,introduced nonlinear weight factor to update the attack radius of the northern goshawk,and used Cauchy-Gaussian hybrid variation to disturb the individual position after one iteration.Secondly,the performance indicators of radar network aiming at the spatial coverage coefficient,namely the coverage rate of responsibility area and the coverage rate of key area,are proposed,and the mathematical model of radar optimization network related to the proposed performance index is established.Finally,the model is solved using the improved sine-cosine algorithm.The simulation results show that the radar network scheme obtained by using this method can achieve the goal of high coverage of the radar network to both the responsibility area and the key area.(2)Aiming at the problem that the above model only considers the index of airspace coverage coefficient under ideal environment and fails to fully consider other factors affecting the actual battlefield environment,a radar optimization networking method based on the improved whale optimization algorithm is proposed.In this method,the four resistance capability of radar network is considered comprehensively,and multiple performance indexes such as airspace coverage coefficient,overlap coefficient,same frequency interference coefficient and resource utilization coefficient are used to establish radar optimization network model in battlefield environment.The whale optimization algorithm with fast convergence speed is adopted to improve the standard whale optimization algorithm by using the optimal point set initialization,elite pool strategy,nonlinear adaptive weight and elite individual adaptive distributed disturbance strategy.The simulation results show that the proposed method can obtain a better networking scheme,so that the radar network can achieve the purpose of high coverage,high overlap,low waste of resources and low interference in the same frequency.(3)Aiming at the problems of enemy suppression interference and terrain limitation in battlefield environment,a radar optimization networking method based on improved sine cosine algorithm is proposed.This method takes the enemy suppression interference in the real battlefield environment as the background,and considers the terrain constraints during the radar deployment,and establishes the radar optimization network model by using the indexes such as coverage coefficient and overlap coefficient.The sine cosine algorithm with good balance ability and easy implementation is selected to improve the standard sine cosine algorithm by using reverse learning strategy,Levy flight strategy,nonlinear control parameter adjustment and polynomial variation.The simulation results show that this method can provide the optimal networking scheme under the background of interference environment and terrain constraints,and achieve the purpose of reasonable allocation and overall consideration of each region. |
