RadarsBEV: A Joint Multi-Radar Fusion and Target Detection Network via Gaussian Attention in Arbitrary Configurations.
Saved in:
| Title: | RadarsBEV: A Joint Multi-Radar Fusion and Target Detection Network via Gaussian Attention in Arbitrary Configurations. |
|---|---|
| Authors: | Guo, Zuyuan1 (AUTHOR), Li, Wujun1,2 (AUTHOR) liwujun@uestc.edu.cn, Zhang, Guoxin1 (AUTHOR), Li, Hongfu1,2 (AUTHOR), He, Jiesong1 (AUTHOR), Teh, Kah Chan2 (AUTHOR), Yi, Wei1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1290. 28p. |
| Subjects: | Multisensor data fusion, Radar signal processing, Generalization, Modular design, Robust control, Target acquisition |
| Abstract: | Highlights: What are the main findings? The RadarsBEV framework introduces a scalable, modular architecture that decouples per-sensor feature extraction from the central fusion process, enabling permutation-invariant multi-radar perception. A novel physics-aware Gaussian cross-attention mechanism explicitly models radar target spatial uncertainty, effectively decoupling feature sampling from high-intensity environmental clutter. What are the implications of the main findings? The decoupled architectural design inherently guarantees graceful degradation, allowing the system to maintain continuous, high-accuracy target detection even during abrupt sensor dropouts. By relying on physical signal priors rather than rigid network topologies, the model achieves robust zero-shot generalization across entirely unseen sensor layouts and degraded operational environments without any retraining. Multi-radar fusion is fundamental for robust, all-weather perception for diverse applications. However, current fusion paradigms face structural and computational bottlenecks. Traditional statistical frameworks suffer from an explosion of dimensional calculation, where computational complexity scales with the number of active sensor nodes. Concurrently, existing statistical and deep learning fusion models exhibit systemic brittleness; their rigid topological binding to predefined sensor counts leads to a drop in performance during sensor dropouts. Furthermore, generic attention mechanisms suffer a phenomenological mismatch with radar signals, neglecting the spatial features of radar targets and leading to false alarms. To overcome these limitations, we propose RadarsBEV, a scalable end-to-end multi-radar detection framework. By decoupling per-sensor feature extraction from the central spatial fusion process, RadarsBEV achieves permutation invariance. This design breaks the scalability limit and enables graceful degradation utilizing residual nodes without system downtime. Crucially, we introduce a physics-aware Gaussian cross-attention mechanism. By guiding sparse feature sampling through predicted two-dimensional Gaussian target geometry, this mechanism decouples attention weights from clutter signal. Extensive experiments on high-fidelity simulations and real-world datasets demonstrate that RadarsBEV achieves better detection performance. Notably, the framework exhibits robust configuration zero-shot generalization, adapting to entirely unseen spatial layouts and degraded operational environments without fine-tuning. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
