JAF-MTT: A Jerk-Aware Multi-Feature Fusion Algorithm for Maneuvering Target Tracking

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In maneuvering target tracking, traditional model-driven tracking algorithms require a predefined target motion model. The estimation accuracy degrades significantly when the actual target maneuver does not match the model assumption. Data-driven tracking algorithms can learn motion patterns directly from trajectory data, making them more robust to complex maneuvers. To improve the tracking performance in high-maneuver scenarios, this paper proposes a jerk-aware multi-feature fusion algorithm for maneuvering target tracking (JAF-MTT). The algorithm adopts jerk as the indicator of maneuver intensity. A parallel structure of convolution and multi-head self-attention is introduced to extract local and global trajectory features. These extracted features are adaptively fused in accordance with maneuver intensity. Finally, a bidirectional LSTM decodes the fused features to derive target state estimation, with the jerk adaptively modulating the gating response. Simulation results demonstrate that the performance of the proposed algorithm is better than that of the compared algorithms in high-maneuver scenarios. Moreover, the proposed algorithm maintains low tracking error under strong measurement noise.