AI 中文总结
EM-KalmanNet是一种AI辅助跟踪算法，通过展开类EM迭代构建可训练架构，实现分块时变状态空间模型的自适应平滑，在多场景实验中性能优于基准方法且推理延迟更低。
AI 中文摘要
当状态空间（SS）模型的动态或观测模型在短数据块间发生变化时，部分已知状态空间模型中的状态估计极具挑战性。经典的基于模型的方法，如期望最大化（EM）卡尔曼滤波器，可联合恢复潜在状态与未知模型参数，但依赖于需准确描述系统的线性-高斯假设，且需要大量前向-后向传递，因此在复杂且非平稳的真实场景下，其性能与计算效率可能下降。另一方面，学习型卡尔曼平滑器对模型失配具有鲁棒性，但在推理阶段无法在无标注数据的情况下适配未见的模型变化。本研究提出EM-KalmanNet，一种用于分块时变SS模型自适应平滑的AI辅助跟踪算法。该方法将固定数量的类EM迭代展开为可训练架构：参数感知型RTSNet基于当前模型参数估计实现学习型E步，轻量型M-Net利用经验矩、残差及梯度相关统计量更新状态转移矩阵或观测矩阵，实现学习型M步。两个模块在展开迭代间共享，通过专用的三阶段流程离线训练。部署阶段，每块的参数估计在连续块间传播，无需在线标注数据或噪声统计知识即可实现观测驱动的自适应。涉及线性与非线性模型、高斯与非高斯噪声、洛伦兹吸引子跟踪及声源定位的实验表明，EM-KalmanNet在持续优于基于模型与数据驱动基准的同时，相比EM-KF大幅降低了推理延迟。
英文摘要
State estimation in partially known state space (SS) models is challenging when the dynamics or observation model varies across short data blocks. Classical model-based approaches, such as the expectation-maximization (EM) Kalman filter, jointly recover the latent states and the unknown model parameters, but rely on linear-Gaussian assumptions that should accurately describe the system and require numerous forward-backward passes. Consequently, their performance and computational efficiency may deteriorate under complex and non-stationary real-world conditions. On the other hand, learned Kalman smoothers are robust to model mismatch yet cannot adapt at inference to unseen model variations without labeled data. In this work, we propose EM-KalmanNet, an AI-aided tracking algorithm for adaptive smoothing in blockwise time-varying SS models. The method unfolds a fixed, small number of EM-like iterations into a trainable architecture: a parameter-aware RTSNet implements a learned E-step conditioned on the current model-parameter estimate, while a lightweight M-Net implements a learned M-step that updates the state-transition or the observation matrix using empirical moments, residuals, and gradient-related statistics. The two modules are shared across the unfolded iterations and are trained offline via a dedicated three-stage procedure. During deployment, the per-block parameter estimate is propagated between consecutive blocks, enabling observation-driven adaptation without labeled online data or knowledge of the noise statistics. Experiments involving linear and nonlinear models, Gaussian and non-Gaussian noise, Lorenz attractor tracking, and acoustic source localization demonstrate that EM-KalmanNet consistently outperforms model-based and data-driven benchmarks while substantially reducing inference latency relative to the EM-KF.
