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This paper introduces IMMNet, a hybrid algorithm that combines the interpretable structure of the interacting multiple model (IMM) with learnable neural components to enhance maneuvering target tracking in three-dimensional space. By integrating model-based and data-driven approaches, IMMNet retains the essential Bayesian inference mechanism while adapting to dynamic motion patterns and noise characteristics from data. Extensive experiments show that IMMNet consistently outperforms existing algorithms, establishing it as a robust and interpretable solution for complex tracking scenarios.
IMMNet outperforms traditional tracking methods by seamlessly blending model-based inference with adaptive learning from data, offering a robust solution 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 outperforms the existing algorithms across various scenarios, validating it as a robust, interpretable, and practical solution for maneuvering target tracking.