融合模型与数据驱动的跟踪算法，提升复杂机动目标追踪精度。
IMMNet: Hybrid Fusion of Model-based and Data-driven Approaches for Maneuvering Target Tracking
- 结合交互多模型结构与可学习神经组件，兼顾可解释性与自适应能力。
- 在多种场景下均优于现有方法，保持实时雷达应用所需的贝叶斯推理机制。
- 适合需要高可靠性与可解释性的雷达/导航系统开发者使用。
三维空间中的机动目标跟踪因运动动态复杂和模型失配仍具挑战性。本文提出一种混合模型/数据驱动算法 IMMNet，将可解释的交互多模型（IMM）结构与可学习的神经组件结合。不同于端到端黑箱方法，IMMNet不仅保留了实时雷达应用必需的贝叶斯推断机制，还能从数据中自适应学习运动模式和噪声特性。大量实验表明，所提算法在各类场景下持续优于现有方法，验证其作为鲁棒、可解释且实用的机动目标跟踪解决方案的有效性。
原文摘要 · Abstract (English)
Maneuvering target tracking in three-dimensional space remains a challenging problem due to complex motion dynamics and model mismatch. To address this, this paper proposes a hybrid model/data-driven algorithm named IMMNet, which integrates the interpretable structure of the interacting multiple model (IMM) algorithm with learnable neural components. Unlike end-to-end black-box methods, the proposed IMMNet algorithm not only can preserve the Bayesian inference mechanism that is essential for real-time radar applications, but also can adaptively learn motion patterns and noise characteristics from data. Extensive experiments demonstrate that the proposed IMMNet algorithm consistently outperforms the existing algorithms across various scenarios, validating it as a robust, interpretable, and practical solution for maneuvering target tracking.
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