Moving Target Detection Method Based on Range? Doppler Domain Compensation and Cancellation for UAV-Mounted Radar

Combining unmanned aerial vehicle (UAV) with through-the-wall radar can realize moving targets detection in complex building scenes. However, clutters generated by obstacles and static objects are always stronger and non-stationary, which results in heavy impacts on moving targets detection. To address this issue, this paper proposes a moving target detection method based on Range-Doppler domain compensation and cancellation for UAV mounted dual channel radar. In the proposed method, phase compensation is performed on the dual channel in range-Doppler domain and then cancellation is utilized to achieve roughly clutters suppression. Next, a filter is constructed based on the cancellation result and the raw echoes, which is used to suppress stationary clutter furthermore. Finally, mismatch imaging is used to focus moving target for detection. Both simulation and UAV-based experiment results are analyzed to verify the efficacy and practicability of the proposed method.

Diversifying Query: Region-Guided Transformer for Temporal Sentence Grounding

Temporal sentence grounding is a challenging task that aims to localize the moment spans relevant to a language description. Although recent DETR-based models have achieved notable progress by leveraging multiple learnable moment queries, they suffer from overlapped and redundant proposals, leading to inaccurate predictions. We attribute this limitation to the lack of task-related guidance for the learnable queries to serve a specific mode. Furthermore, the complex solution space generated by variable and open-vocabulary language descriptions exacerbates the optimization difficulty, making it harder for learnable queries to distinguish each other adaptively. To tackle this limitation, we present a Region-Guided TRansformer (RGTR) for temporal sentence grounding, which diversifies moment queries to eliminate overlapped and redundant predictions. Instead of using learnable queries, RGTR adopts a set of anchor pairs as moment queries to introduce explicit regional guidance. Each anchor pair takes charge of moment prediction for a specific temporal region, which reduces the optimization difficulty and ensures the diversity of the final predictions. In addition, we design an IoU-aware scoring head to improve proposal quality. Extensive experiments demonstrate the effectiveness of RGTR, outperforming state-of-the-art methods on QVHighlights, Charades-STA and TACoS datasets.