With the increasing complexity of earth observation tasks, the single observation mode can no longer meet the current requirements for timeliness and accuracy. The joint use of multi-platform observation resources can give full play to the advantages of each platform and improve the efficiency of task execution. The research on joint task planning of multi-platform is the key to achieve the collaborative effect of multi-platform earth observation system. In this paper, a unified observation path model is proposed. The objective functions of tracking rate, coverage rate, recognition rate and surveillance rate are proposed respectively for moving target tracking, regional target search, multi-point target recognition and continuous target surveillance. To address the problem, a problem-specific knowledge-based memetic algorithm is proposed. The mechanism of combining swarm intelligence algorithm and local search algorithm in the framework of memetic algorithm is used to enhance the ability of exploration and exploitation. Problem-specific knowledge operators are introduced into global search and local search. It can increase the learning ability of the algorithm in the iterative process, and guide the algorithm to adopt reasonable evolutionary strategy. Finally, speed up convergence while avoiding premature maturity. Simulation experiments verify the superiority of the algorithm.

随着对地观测任务的日益复杂，单一的观测模式已不能满足当前对时效性和准确性的要求。多平台观测资源的联合使用，可以充分发挥各平台的优势，提高任务执行效率。多平台联合任务规划研究是实现多平台对地观测系统协同效应的关键。本文提出了统一的观测路径模型。分别针对运动目标跟踪、区域目标搜索、多点目标识别和连续目标监视提出了跟踪率、覆盖率、识别率和监视率的目标函数。为了解决这个问题，提出了一种基于问题特定知识的模因算法。在模因算法的框架内，采用群体智能算法和局部搜索算法相结合的机制，增强探索和开发的能力。问题特定的知识算子被引入全局搜索和局部搜索。它可以增加算法在迭代过程中的学习能力，引导算法采用合理的进化策略。最后，在避免过早成熟的同时加快收敛速度​​。仿真实验验证了算法的优越性。它可以增加算法在迭代过程中的学习能力，引导算法采用合理的进化策略。最后，在避免过早成熟的同时加快收敛速度​​。仿真实验验证了算法的优越性。它可以增加算法在迭代过程中的学习能力，引导算法采用合理的进化策略。最后，在避免过早成熟的同时加快收敛速度​​。仿真实验验证了算法的优越性。