论文标题
通过预测编码，通过寻求跨击的导弹对导弹的自适应量表因子补偿
Adaptive Scale Factor Compensation for Missiles with Strapdown Seekers via Predictive Coding
论文作者
论文摘要
在这项工作中，我们提出了一种自适应补偿旋转速度和寻求器角度测量值的比例因子误差的方法。适应方案使用以复发层的深度神经网络实现的预测编码模型来估算规模因子误差，然后使用这些估计来补偿错误。在培训期间，该模型将学习各种规模因子错误，理想地绑定了部署过程中可能发生的预期错误，从而使部署模型可以实时快速适应地面真相错误。我们在近六个自由度的模拟中证明了exoatmospheric截距，我们的方法有效地补偿了并发的旋转速度和寻求者角度尺度因子误差。薪酬方法是普遍的，因为它独立于给定的指导，导航和控制系统实施。尽管该方法使用带有式式跨度和跨dise击的Endotospheric导弹以及寻求跨式跨down的Endopospheric导弹以及通用惯性测量单位速率Gyro补偿也适用。
In this work we present a method to adaptively compensate for scale factor errors in both rotational velocity and seeker angle measurements. The adaptation scheme estimates the scale factor errors using a predictive coding model implemented as a deep neural network with recurrent layer, and then uses these estimates to compensate for the error. During training, the model learns over a wide range of scale factor errors that ideally bound the expected errors that can occur during deployment, allowing the deployed model to quickly adapt in real time to the ground truth error. We demonstrate in a realistic six degrees-of-freedom simulation of an exoatmospheric intercept that our method effectively compensates for concurrent rotational velocity and seeker angle scale factor errors. The compensation method is general in that it is independent of a given guidance, navigation, and control system implementation. Although demonstrated using an exoatmospheric missile with strapdown seeker, the method is also applicable to endoatmospheric missiles with both gimbaled and strapdown seekers, as well as general purpose inertial measurement unit rate gyro compensation.
