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Adaptive Spectral Denoising Network for Efficient Maneuvering Target Tracking
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AI
DOI：10.1109/TIM.2025.3617392.png)
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Abstract
En 中文
Maneuvering target tracking remains a critical, yet challenging task, due to the unpredictable and varying motion patterns of targets. Although learning-based methods have shown promise by directly learning the nonlinear mappings from noisy observations to target states by integrating both long- and short-range dependencies, existing advanced Transformer-based models struggle with robustness to noise, computational efficiency, and capturing fine-grained temporal dependencies. To address these, we propose the adaptive spectral denoising network (ASDN), comprising an adaptive spectral denoising filter (ASDF) and a temporal local-pattern guided block (TLGB). The ASDF processes signals in the frequency domain through the Fourier transform to enhance feature representation by simultaneously capturing long- and short-range dependencies. This is achieved through adaptive spectral masking that suppresses noise-corrupted frequency components while preserving critical dependencies. To compensate for potential information loss caused by such spectral masking, the TLGB explicitly models short-term temporal interactions to preserve local dynamics critical for accurate tracking. Furthermore, we introduce large-scale maneuvering target trajectory (LASTv2), a large-scale dataset of maneuvering target trajectories characterized by diverse and complex motion patterns, specifically designed to simulate real-world adversarial scenarios with high mobility and stochastic dynamics. Experimental results demonstrate that our method achieves state-of-the-art performance, reducing root-mean-square error by 76.9% and improving inference speed by two orders of magnitude compared to the baseline methods.
Keywords:
Target tracking
Trajectory
Radar tracking
Noise
Transformers
Noise reduction
Time series analysis
Radar
Computational modeling
Adaptive systems
Adaptive spectral denoising
flight trajectory dataset
maneuvering target tracking
radar target
temporal local-pattern guided tracking
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5.9
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1.9W
Citations:
5.8W
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