Abstract. Image navigation and registration (INR) for the GOES-R series advanced baseline imager (ABI) is performed in ground processing of the raw image data. A Kalman filter processes ABI star measurements to compute time-varying optical misalignment parameters that allow real-time alignment of image samples to the GOES-R Fixed Grid. For the system to meet INR requirements, the filter measurements noise covariance (R) and process noise covariance (Q) matrices must closely approximate behavior of the true system. Prelaunch estimates of the Q and R parameters did not match actual GOES-16 performance. Several attempts to manually determine improved Q&R parameters were not completely successful. Instead, the Kalman filter was calibrated (tuned) using maximum likelihood (ML) estimation of the parameters: computed utilizing partial derivatives of the Kalman equations, Gauss–Newton iteration, and nonlinear optimization logic. The updated parameters significantly improved performance and are currently operational for GOES-16 and 17. ML estimation of Kalman filter parameters has been used successfully for other systems but has not widely been documented. Greater use is encouraged. We also include a review of ABI INR theory and implementation.

中文翻译：

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使用最大似然参数估计改进 GOES-R ABI 图像导航和配准

摘要。GOES-R 系列高级基线成像仪 (ABI) 的图像导航和配准 (INR) 在原始图像数据的地面处理中执行。卡尔曼滤波器处理 ABI 星测量以计算随时间变化的光学未对准参数，从而允许将图像样本实时对准 GOES-R 固定网格。为了使系统满足 INR 要求，滤波器测量噪声协方差 (R) 和过程噪声协方差 (Q) 矩阵必须非常接近真实系统的行为。Q 和 R 参数的发射前估计与实际的 GOES-16 性能不匹配。多次尝试手动确定改进的 Q&R 参数并没有完全成功。相反，使用参数的最大似然 (ML) 估计校准（调整）卡尔曼滤波器：利用卡尔曼方程的偏导数、高斯-牛顿迭代和非线性优化逻辑计算。更新后的参数显着提高了性能，目前可用于 GOES-16 和 17。卡尔曼滤波器参数的 ML 估计已成功用于其他系统，但尚未被广泛记录。鼓励更多地使用。我们还包括对 ABI INR 理论和实施的回顾。