The rapidity and accuracy of the initial alignment influence the performance of the strapdown inertial navigation system(SINS),compass alignment is one of the most important methods for initial alignment.The selection...The rapidity and accuracy of the initial alignment influence the performance of the strapdown inertial navigation system(SINS),compass alignment is one of the most important methods for initial alignment.The selection of the parameters of the compass alignment loop directly affects the result of alignment.Nevertheless,the optimal parameters of the compass loop of different SINS are also different Traditionally,the alignment parameters are determined by experience and trial-and-error,thus it cannot ensure that the parameters are optimal.In this paper,the Genetic Algorithm-Particle Swarm Optimization(GA-PSO) algorithm is proposed to optimize the compass alignment parameters so as to improve the performance of the initial alignment of strapdown gyrocompass.The experiment results showed that the GA-PSO algorithm can find out the optimal parameters of the compass alignment circuit quickly and accurately and proved the effectiveness of the proposed method.展开更多
随着各种新型雷达的出现或战时预留模式的采用,真实的战场电磁环境将越加复杂,大概率会出现种类未知且参数突变的雷达调制信号,对现有的调制方式识别算法带来严峻挑战。对此,分析雷达调制方式“未知”对识别结果的影响机理,将开集差分...随着各种新型雷达的出现或战时预留模式的采用,真实的战场电磁环境将越加复杂,大概率会出现种类未知且参数突变的雷达调制信号,对现有的调制方式识别算法带来严峻挑战。对此,分析雷达调制方式“未知”对识别结果的影响机理,将开集差分分布对齐(distribution alignment with open set difference,DAOD)算法引入雷达调制方式识别领域,设计具体应用的技术方案,并针对DAOD算法所需参数依靠先验知识或者试探选取问题,利用蜣螂优化(dung beetle optimizer,DBO)算法进行参数优化。仿真结果表明:在单个雷达调制方式未知情形下,精确度Accuracy和F-measure分值的平均值分别可达91.34%和95.11%;在多个雷达调制方式未知情形下,Accuracy和F-measure的平均值分别可达91.37%、93.69%;与DAOD算法相比,上述结果分别提升了3.77%、1.83%、21.17%和12.06%。因此,DBO-DAOD算法可有效提升未知雷达调制方式的识别率。展开更多
Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qu...Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.展开更多
基金Research supported by the National Natural Science Foundation of China(Nos.4157069,41404002 and 61503404)the National Key Research and Development Program(2016YFB0501700,2016YFB0501701)。
文摘The rapidity and accuracy of the initial alignment influence the performance of the strapdown inertial navigation system(SINS),compass alignment is one of the most important methods for initial alignment.The selection of the parameters of the compass alignment loop directly affects the result of alignment.Nevertheless,the optimal parameters of the compass loop of different SINS are also different Traditionally,the alignment parameters are determined by experience and trial-and-error,thus it cannot ensure that the parameters are optimal.In this paper,the Genetic Algorithm-Particle Swarm Optimization(GA-PSO) algorithm is proposed to optimize the compass alignment parameters so as to improve the performance of the initial alignment of strapdown gyrocompass.The experiment results showed that the GA-PSO algorithm can find out the optimal parameters of the compass alignment circuit quickly and accurately and proved the effectiveness of the proposed method.
文摘随着各种新型雷达的出现或战时预留模式的采用,真实的战场电磁环境将越加复杂,大概率会出现种类未知且参数突变的雷达调制信号,对现有的调制方式识别算法带来严峻挑战。对此,分析雷达调制方式“未知”对识别结果的影响机理,将开集差分分布对齐(distribution alignment with open set difference,DAOD)算法引入雷达调制方式识别领域,设计具体应用的技术方案,并针对DAOD算法所需参数依靠先验知识或者试探选取问题,利用蜣螂优化(dung beetle optimizer,DBO)算法进行参数优化。仿真结果表明:在单个雷达调制方式未知情形下,精确度Accuracy和F-measure分值的平均值分别可达91.34%和95.11%;在多个雷达调制方式未知情形下,Accuracy和F-measure的平均值分别可达91.37%、93.69%;与DAOD算法相比,上述结果分别提升了3.77%、1.83%、21.17%和12.06%。因此,DBO-DAOD算法可有效提升未知雷达调制方式的识别率。
基金supported by the National Natural Science Foundation of China(61573017 61703425)+2 种基金the Aeronautical Science Fund(20175796014)the Shaanxi Province Natural Science Foundation Research Project(2016JQ6062 2017JM6062)
文摘Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.
