IMMNet: Hybrid Fusion of Model-based and Data-driven Approaches for Maneuvering Target Tracking
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 outpe...
| Authors | Yixuan Zhao, Chaoqun Yang, Lin Gao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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