发表机构
School of Automation, Southeast University; School of ICE, University of Electronic Science and Technology of China; Faculty of Artificial Intelligence, Shanghai University of Electric Power; School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（东南大学自动化学院; 电子科技大学信息与通信工程学院; 上海电力大学人工智能学院; 上海交通大学自动化与智能感知学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对三维空间机动目标跟踪难题，提出IMMNet算法，融合IMM算法可解释结构与神经组件，能保留贝叶斯推理机制并从数据学习，实验证明该算法在多场景下优于现有算法，是机动目标跟踪的有效方案。
AI 中文摘要
三维空间中的机动目标跟踪因复杂运动动力学和模型不匹配仍是一个具有挑战性的问题。本文提出了一种名为IMMNet的混合模型/数据驱动算法，它将交互多模型（IMM）算法的可解释结构与可学习的神经组件相结合。与端到端黑箱方法不同，IMMNet算法不仅能保留对实时雷达应用至关重要的贝叶斯推理机制，还能从数据中自适应学习运动模式和噪声特征。大量实验表明，IMMNet算法在各种场景下始终优于现有算法，验证了它是一种用于机动目标跟踪的强大、可解释且实用的解决方案。
英文摘要
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.
