分析了空间低轨目标群的运行特点,提出了基于时序向量相似性的空间目标群匹配算法,提高了对低轨巨型星座的识别管理能力。首先,介绍了时序向量的降维方法,将目标群高维观测时序向量简化为空间构型序列;而后,提出了基于动态时间规整(Dyna...分析了空间低轨目标群的运行特点,提出了基于时序向量相似性的空间目标群匹配算法,提高了对低轨巨型星座的识别管理能力。首先,介绍了时序向量的降维方法,将目标群高维观测时序向量简化为空间构型序列;而后,提出了基于动态时间规整(Dynamic Time Warping,DTW)的目标群空间构型序列相似性判别算法;最后,利用星链卫星目标群仿真和实测数据对算法的匹配能力进行验证。结果表明该算法可实现空间目标群监测数据快速匹配,仿真数据匹配过程中,在群内目标缺失30%的条件下匹配成功率可达100%,在低缺失条件下(缺失率5%以内)群内目标识别成功率平均超过75%;实测数据匹配成功率可达100%。展开更多
To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes t...To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes three strategies.Firstly,the average crowding distance method is proposed,which takes into account the influence of individuals on the crowding distance and reduces the algorithm’s time complexity and computational cost,ensuring efficient external archive maintenance and improving the algorithm’s distribution.Secondly,the algorithm utilizes particle difference to guide adaptive inertia weights.In this way,the degree of disparity between a particle’s historical optimum and the population’s global optimum is used to determine the value of w.With different degrees of disparity,the size of w is adjusted nonlinearly,improving the algorithm’s convergence.Finally,the algorithm is designed to control the search direction by hierarchically selecting the globally optimal policy,which can avoid a single search direction and eliminate the lack of a random search direction,making the selection of the global optimal position more objective and comprehensive,and further improving the convergence of the algorithm.The MOPSO-MS is tested against seven other algorithms on the ZDT and DTLZ test functions,and the results show that the MOPSO-MS has significant advantages in terms of convergence and distributivity.展开更多
文摘分析了空间低轨目标群的运行特点,提出了基于时序向量相似性的空间目标群匹配算法,提高了对低轨巨型星座的识别管理能力。首先,介绍了时序向量的降维方法,将目标群高维观测时序向量简化为空间构型序列;而后,提出了基于动态时间规整(Dynamic Time Warping,DTW)的目标群空间构型序列相似性判别算法;最后,利用星链卫星目标群仿真和实测数据对算法的匹配能力进行验证。结果表明该算法可实现空间目标群监测数据快速匹配,仿真数据匹配过程中,在群内目标缺失30%的条件下匹配成功率可达100%,在低缺失条件下(缺失率5%以内)群内目标识别成功率平均超过75%;实测数据匹配成功率可达100%。
基金National Natural Science Foundation of China(No.61702006)Open Fund of Key laboratory of Anhui Higher Education Institutes(No.CS2021-ZD01)。
文摘To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes three strategies.Firstly,the average crowding distance method is proposed,which takes into account the influence of individuals on the crowding distance and reduces the algorithm’s time complexity and computational cost,ensuring efficient external archive maintenance and improving the algorithm’s distribution.Secondly,the algorithm utilizes particle difference to guide adaptive inertia weights.In this way,the degree of disparity between a particle’s historical optimum and the population’s global optimum is used to determine the value of w.With different degrees of disparity,the size of w is adjusted nonlinearly,improving the algorithm’s convergence.Finally,the algorithm is designed to control the search direction by hierarchically selecting the globally optimal policy,which can avoid a single search direction and eliminate the lack of a random search direction,making the selection of the global optimal position more objective and comprehensive,and further improving the convergence of the algorithm.The MOPSO-MS is tested against seven other algorithms on the ZDT and DTLZ test functions,and the results show that the MOPSO-MS has significant advantages in terms of convergence and distributivity.
