Disconnection in the distributed heterogeneous networked unmanned weapon systems is caused by multiple weapon units＇ failure. The technical routes were analyzed to achieve resilience in the disconnection situation. A...Disconnection in the distributed heterogeneous networked unmanned weapon systems is caused by multiple weapon units＇ failure. The technical routes were analyzed to achieve resilience in the disconnection situation. A heterogeneous distributed network model of networked unmanned weapon systems was established. And an approach of adding relay weapon units was proposed to a- chieve fault tolerance after weapon units＇ failure due to attack or energy exhaustion. An improved ge- netic algorithm was proposed to determine and optimize the position of the relay weapon units. Simulation results in the MATLAB show that the improved resilience-based genetic algorithm can restore the network connection maximally when the number of relay units is limited, the network can keep on working after failure, and the implementation cost is controlled in a reasonable range.展开更多
In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input represen...In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.展开更多
基金Supported by the Aviation Science Foundation of China(2013ZC72006)
文摘Disconnection in the distributed heterogeneous networked unmanned weapon systems is caused by multiple weapon units＇ failure. The technical routes were analyzed to achieve resilience in the disconnection situation. A heterogeneous distributed network model of networked unmanned weapon systems was established. And an approach of adding relay weapon units was proposed to a- chieve fault tolerance after weapon units＇ failure due to attack or energy exhaustion. An improved ge- netic algorithm was proposed to determine and optimize the position of the relay weapon units. Simulation results in the MATLAB show that the improved resilience-based genetic algorithm can restore the network connection maximally when the number of relay units is limited, the network can keep on working after failure, and the implementation cost is controlled in a reasonable range.
基金Supported by the National Natural Science Foundation of China(11102080,61374212)the Science and Technology on Electro-Optic Control Laboratory and Aeronautical Science Foundation of China(20135152047)
文摘In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.
