A Background-Aware Multimodal Framework for Radar Small-Target Detection in Complex Environments

Radar small-target detection in complex low-altitude environments remains challenging because weak target echoes are easily masked by heterogeneous clutter, multipath returns, and environment-dependent interference. To improve target-clutter separability, this article proposes a background-aware multimodal framework that jointly exploits range-Doppler signal structure information (SSI) and satellite-derived environmental priors. Instead of making decisions from isolated range-Doppler cells or directly fusing radar and map images, the proposed method reformulates detection as a target-centric structural discrimination problem. Candidate locations are first generated by a low-threshold constant false alarm rate (CFAR) detector, and local SSI patches are extracted to preserve target-induced range-Doppler morphology. A physics-inspired SSI enhancement module is designed to encode log-compressed magnitude, local background contrast, and range- and Doppler-domain structural gradients. Meanwhile, spatially aligned satellite images and land-cover masks are used to construct environmental priors for characterizing local clutter conditions. An enhanced gated cross-modal fusion module then selectively injects background information into radar SSI features while preserving radar features as the dominant representation. Finally, a multiscale Transformer-based detection head with cross-scale interaction is employed to jointly model fine-grained SSI morphology and broader contextual consistency. Experiments on measured radar data demonstrate that the proposed method outperforms conventional CFAR, representative CNN-, Transformer-, and graph-based detectors, as well as a previous radar-satellite fusion method, especially in low-false-alarm operating regions. Ablation studies, information-scale experiments, and signal-to-clutter-and-interference ratio-level analysis further verify the effectiveness of the proposed components and the importance of appropriate SSI and environmental context scales.