用频域加权提升机器人状态估计抗干扰能力
FW-NKF: Frequency-Weighted Neural Kalman Filters
- 在卡尔曼滤波残差中加入因果谱形模块，动态抑制特定频段噪声
- 在洛伦兹系统与惯性姿态估计上，定位误差降低10%，朝向精度显著提升
- 适合处理传感器振动、电磁干扰等周期性噪声场景的机器人系统
鲁棒的状态估计是机器人自主性的核心，但经典卡尔曼滤波难以应对频率相关扰动和模型失配问题，如传感器振动、电磁干扰和周期性噪声。尽管深度卡尔曼滤波（DKF）通过学习隐状态转移扩展了扩展卡尔曼滤波（EKF）框架，却缺乏显式抑制带限噪声成分的能力，而这类噪声在真实场景中常污染传感器测量。本文提出频域加权神经卡尔曼滤波器（FW-NKF），一种统一的混合方法，将因果谱形算子嵌入卡尔曼测量残差，并联合学习观测与转移网络。通过同时调整滤波器频谱与隐状态表示，FW-NKF有效衰减噪声主导的频段，同时捕捉复杂残差结构。我们在四个异构基准上进行大量实验，涵盖多维洛伦兹系统等混沌系统及全身惯性姿态估计，结果显示定位误差最多降低10%，朝向精度明显改善。消融实验确认频域加权与深度隐状态建模对整体性能有贡献。
原文摘要 · Abstract (English)
Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch such as sensor vibrations, electromagnetic interference, and periodic noise. Although Deep Kalman Filter (DKF) variants extend the Extended Kalman Filtering (EKF) framework by learning latent transitions, they lack explicit mechanisms to suppress band-limited noise components that typically corrupt sensor measurements in real-world scenarios. We introduce the Frequency-Weighted Neural Kalman Filter (FW-NKF), a unified hybrid approach that embeds a causal spectral-shaping operator into the Kalman measurement residual and jointly learns observation, and transition networks. By adapting both the filter spectrum and the latent state representation, FW-NKF attenuates the noise-dominated frequency bands while capturing complex residual structures. We conduct extensive experiments on four heterogeneous benchmarks, including chaotic systems such as multi-dimensional Lorenz systems and full-body inertial pose estimation, and find a reduction in localization error of up to 10% as well as marked improvements in orientation accuracy. Our ablation studies confirm that frequency weighting and deep latent-state modeling contribute to overall performance.
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