无人机集群作战中连续时间Markov链模型的求解方法

针对无人机集群目标作战解析建模时在状态转移过程中计算速率低的问题,提出了一种基于行压缩存储的四阶Runge-Kutta法。根据无人机集群作战样式将无人机集群作战过程划分为三个阶段,并分阶段对无人机集群作战的状态转移过程建立连续时间Markov链模型。以无人机集群完成作战任务的可靠性作为求解指标,运用四阶Runge-Kutta法对Markov模型进行求解。由于求解过程中速率转移矩阵具有稀疏特性,采用基于行压缩存储的算法优化求解速率。仿真实验表明,运用连续时间Markov理论建立的无人机集群作战过程模型的有效性和可行性优于其他模型。同时,与其他算法及模型相比,该算法计算速率更高、能更好地满足结果精度的可靠性需求,进一步说明了本算法的优越性。 〖BHDWG8,WK10YQ,DK1*2,WK1*2D〗〖XCHSC.TIF；%129%129〗听语音 聊科研与作者互动

Solution to continuous time Markov chain model for unmanned aerial vehicle swarm operation

In order to solve the problem of low computing speed in the process of state transition in the analytical modeling of UAV (unmanned aerial vehicle) swarm operation, a fourth-order Runge-Kutta method based on the row compressed storage was proposed. The UAV swarm operation process was divided into three stages according to the UAV swarm operation style, and continuous time Markov chain model was established for the state transition process of UAV swarm operation in stages. In the meantime, taking the reliability of UAV swarm to complete combat mission as the solving index, the fourth-order Runge-Kutta method was used to solve the Markov model, and the method based on row compressed storage was used to optimize the solving rate owing to the sparsity feature of the rate transfer matrix. Simulation results show that the established continuous time Markov chain model has better effectiveness and feasibility than other models. At the same time, compared with other algorithms, the proposed algorithm has higher computing speed and better reliability requirements to meet the accuracy of results, which further shows the superiority of it.