对比测试发现，深度学习滤波器KalmanNet在真实雷达数据上不如传统IMM滤波器。
Performance Evaluation of Deep Learning-Based State Estimation: A Comparative Study of KalmanNet
- 用卡尔曼网络替代传统滤波器，尝试数据驱动状态估计
- 在汽车雷达数据上，KalmanNet误差和不确定性均高于IMM滤波器
- 结果表明当前深度学习滤波器不适用于高安全要求场景
卡尔曼滤波器（KF）是实时状态估计的核心方法，广泛应用于现代驾驶辅助与安全系统中的雷达跟踪。在线性动态系统且噪声服从高斯分布时，KF为最优估计器。然而，现实系统常偏离这些假设。随着深度学习在各领域的成功，基于数据的滤波方法逐渐兴起，常宣称优于传统模型。本文选取代表性模型KalmanNet，基于汽车雷达数据评估其在真实场景下的表现，并与交互多模型（IMM）滤波器对比。评估指标包括原始误差、归一化误差及状态不确定性。结果表明，KalmanNet性能劣于IMM滤波器，说明当前数据驱动滤波方法仍缺乏可靠性和鲁棒性，尚不适用于安全关键应用。
原文摘要 · Abstract (English)
Kalman Filters (KF) are fundamental to real-time state estimation applications, including radar-based tracking systems used in modern driver assistance and safety technologies. In a linear dynamical system with Gaussian noise distributions the KF is the optimal estimator. However, real-world systems often deviate from these assumptions. This deviation combined with the success of deep learning across many disciplines has prompted the exploration of data driven approaches that leverage deep learning for filtering applications. These learned state estimators are often reported to outperform traditional model based systems. In this work, one prevalent model, KalmanNet, was selected and evaluated on automotive radar data to assess its performance under real-world conditions and compare it to an interacting multiple models (IMM) filter. The evaluation is based on raw and normalized errors as well as the state uncertainty. The results demonstrate that KalmanNet is outperformed by the IMM filter and indicate that while data-driven methods such as KalmanNet show promise, their current lack of reliability and robustness makes them unsuited for safety-critical applications.
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