基于预测编码的捷联导引头自适应比例因子补偿
Adaptive Scale Factor Compensation for Missiles with Strapdown Seekers via Predictive Coding
摘要：
在这项工作中，我们提出了一种方法来自适应补偿旋转速度和导引头角度测量的比例因子误差。自适应方案使用实现为具有递归层的深度神经网络的预测编码模型来估计比例因子误差，然后使用这些估计来补偿误差。在训练过程中，模型在广泛的比例因子误差范围内进行学习，这些误差理想地限制了部署过程中可能发生的预期误差，从而使部署的模型能够实时快速适应地面实况误差。我们证明了在一个现实的六度的自由度模拟大气层外拦截，我们的方法有效地补偿并发的旋转速度和导引头角度比例因子误差。补偿方法是通用的，因为它独立于给定的制导、导航和控制系统的实现。虽然演示使用大气层外导弹与捷联式导引头，该方法也适用于大气层内导弹与万向节和捷联式导引头，以及通用的惯性测量单位速率陀螺仪补偿。
英文摘要：
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.
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