Adaptive Spectral Denoising Network for Efficient Maneuvering Target Tracking

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 non-linear 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 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 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.