The Unscented Kalman Filter (UKF) is a ubiquitous tool for nonlinear state estimation; however, its performance is limited by the static parameterization of the Unscented Transform (UT). Conventional weighting schemes, governed by fixed scaling parameters, assume implicit Gaussianity and fail to adapt to time-varying dynamics or heavy-tailed measurement noise. This work introduces the Meta-Adaptive UKF (MA-UKF), a framework that reformulates sigma-point weight synthesis as a hyperparameter optimization problem addressed via memory-augmented meta-learning. Unlike standard adaptive filters that rely on instantaneous heuristic corrections, our approach employs a Recurrent Context Encoder to compress the history of measurement innovations into a compact latent embedding. This embedding informs a policy network that dynamically synthesizes the mean and covariance weights of the sigma points at each time step, effectively governing the filter's trust in the prediction versus the measurement. By optimizing the system end-to-end through the filter's recursive logic, the MA-UKF learns to maximize tracking accuracy while maintaining estimation consistency. Numerical benchmarks on maneuvering targets demonstrate that the MA-UKF significantly outperforms standard baselines, exhibiting superior robustness to non-Gaussian glint noise and effective generalization to out-of-distribution (OOD) dynamic regimes unseen during training.

翻译：无迹卡尔曼滤波器（UKF）是非线性状态估计中普遍使用的工具；然而，其性能受限于无迹变换（UT）的静态参数化。由固定缩放参数控制的传统加权方案隐含地假设高斯性，无法适应时变动力学或重尾测量噪声。本文提出了元自适应UKF（MA-UKF）框架，该框架将Sigma点权重合成重新表述为通过记忆增强元学习解决的超参数优化问题。与依赖瞬时启发式校正的标准自适应滤波器不同，我们的方法采用循环上下文编码器将测量新息的历史压缩为紧凑的潜在嵌入。该嵌入信息输入至策略网络，该网络在每个时间步动态合成Sigma点的均值和协方差权重，从而有效调控滤波器对预测与测量的信任度。通过滤波器的递归逻辑对系统进行端到端优化，MA-UKF能够学习在保持估计一致性的同时最大化跟踪精度。针对机动目标的数值基准测试表明，MA-UKF显著优于标准基线方法，对非高斯闪烁噪声表现出卓越的鲁棒性，并能有效泛化至训练中未见的分布外（OOD）动态状态。