基于深度神经网络的空中目标作战意图识别

传统基于空中目标特征状态推理作战意图的方法，需要大量的领域专家知识对特征状态的权重、先验概率等进行量化，明确特征状态与意图之间的对应关系，而神经网络可以在领域专家知识不足条件下，通过自身训练得到特征状态与意图之间的规则。针对反向传播（BP）算法在更新网络节点权值时收敛速度慢、容易陷入局部最优的问题，通过引入ReLU（Rectified Linear Unit）激活函数和自适应矩估计（Adam）优化算法，设计了基于深度神经网络的作战意图识别模型，提高了模型收敛速度，有效地防止陷入局部最优。仿真结果表明，所提方法能够有效识别空中目标作战意图，获得更高的识别率。

Combat intention recognition for aerial targets based on deep neural network

Based on the state feature of the target, traditional methods of inferring aerial targets’ combat intention, require a large amount of domain expert knowledge to quantify the weight of feature, prior probability and clear correspondence between state characteristics and intention. However, the neural network method can train itself to obtain the rules between state features and intentions in the absence of domain expert knowledge. The Back Propagation (BP) algorithm has slow convergence rate and can easily fall into local optimum when updating the network node weights. Through the introduction of the Rectified Linear Unit (ReLU) activation function and the Adaptive moment estimation (Adam) optimization algorithm, a combat intention recognition model based on deep neural network is designed to improve the convergence speed of the model and effectively prevent the algorithm from falling into a local optimum. The simulation results show that the proposed method can effectively recognise the aerial targets’ combat intention and obtain a higher recognition rate.