The precise detection and localization of 'low-slow-small' (LSS) unmanned aerial
vehicles (UAVs) pose significant challenges in terminal defense scenarios. These
non-cooperative targets have extremely sparse representations in radar point
clouds because of their minimal radar cross-sections and appear as textureless,
blurred patches in visual images. Furthermore, traditional multi-modal fusion
paradigms rely heavily on rigid geometric projection matrices, which frequently
fail under the mechanical vibrations, thermal deformations, and extrinsic
perturbations inherent in rapid deployment environments, which often results in
severe feature misalignment and contamination. To overcome these critical
bottlenecks, we present an end-to-end cross-modal detection framework built on
Bidirectional Collaborative Energy Fields (Co-Field). By redefining the 3D
object detection task as an energy field reconstruction process within a latent
multimodal feature space, we entirely remove the need for absolutely precise
extrinsic parameters. Our framework uses a novel bidirectional mutual-guided
interaction mechanism. Specifically, a vision-guided radar branch employs
deformable cross-attention to adaptively aggregate highly discrete radar
geometric features around visual semantic centers, achieving a feature-level
soft alignment that implicitly compensates for calibration drift. Concurrently,
a radar-corrected vision branch utilizes radar-derived geometric occupancy
probabilities as depth anchors to gate and calibrate visual features,
effectively suppressing the inherent divergence of monocular depth estimation.
Ultimately, the framework regresses the target's precise 3D states and
fine-grained categories directly from the extrema of the reconstructed
collaborative energy field, circumventing complex heuristic post-processing.
Experiments on the curated MMAUD dataset show that the proposed Co-Field method
achieves better robustness. It remains highly effective even under severe
calibration errors up to 10 degrees and achieves a 74.2% recall rate at extreme
sparse distances ranging from 70 to 100 meters, and consistently outperforms
state-of-the-art rigid and soft fusion baselines.