论文标题
使用增强元学习的导弹指导的视线曲率
Line of Sight Curvature for Missile Guidance using Reinforcement Meta-Learning
论文作者
论文摘要
我们使用增强元学习来优化视线曲率策略，从而提高了针对机动目标的指导系统的有效性。该策略是作为一个经常性神经网络实施的，该神经网络将导航系统映射到Euler 321态度表示。然后，态度表示形式用于构建一个方向余弦矩阵，该矩阵偏向观察到的视线向量。然后，从偏见的视线中得出的视线旋转速率被映射到指挥系统的指挥加速度。通过改变偏差，随导航系统输出的函数，该策略可以增强对高度操纵目标的准确性。重要的是，我们的方法不需要估计目标加速度。在我们的实验中，我们证明，当我们的方法与比例导航相结合时，该系统的表现明显超过了比例导航，并且对目标加速度的完美了解，从而提高了准确性，而通过对广泛的目标手术进行控制的努力较少。
We use reinforcement meta learning to optimize a line of sight curvature policy that increases the effectiveness of a guidance system against maneuvering targets. The policy is implemented as a recurrent neural network that maps navigation system outputs to a Euler 321 attitude representation. The attitude representation is then used to construct a direction cosine matrix that biases the observed line of sight vector. The line of sight rotation rate derived from the biased line of sight is then mapped to a commanded acceleration by the guidance system. By varying the bias as a function of navigation system outputs, the policy enhances accuracy against highly maneuvering targets. Importantly, our method does not require an estimate of target acceleration. In our experiments, we demonstrate that when our method is combined with proportional navigation, the system significantly outperforms augmented proportional navigation with perfect knowledge of target acceleration, achieving improved accuracy with less control effort against a wide range of target maneuvers.
