AI 中文总结
SymNetPro通过LOS感知注意力偏置和发射器丢弃增强，在稀疏观测下提升定向多发射器定位精度，显著降低OSPA。
AI 中文摘要
从稀疏接收功率观测中进行定向多发射器定位是困难的，因为接收器仅观测到源未分辨的聚合场：多个定向源叠加，建筑物遮挡碎片化其可见区域，且较强源可能掩盖较弱源。我们提出SymNetPro，它保留了SymNet的双任务无线电地图重建和定位骨干，并添加了两个针对性组件。首先，稀疏视线（LOS）感知注意力偏置将遮挡感知的空间关系注入选定的令牌交互中。其次，发射器丢弃增强在移除一个样本支持的发射器后重组训练场景，使模型暴露于受控的源基数变化。在定向射线追踪城市场景上的实验表明，在极端稀疏采样下，其OSPA显著低于代表性定位基线，且在测量噪声和发射器数量增加时具有一致的增益。发射器特定证据分析进一步表明，剩余未命中集中在目标对聚合观测贡献的可区分功率很小的区域。
英文摘要
Directional multi-transmitter localization from sparse received-power observations is difficult because the receiver observes only the source-unresolved aggregate field: multiple directional sources superpose, building blockage fragments their visible regions, and stronger sources can mask weaker ones. We present SymNetPro, which retains the dual-task radio-map reconstruction and localization backbone of SymNet and adds two targeted components. First, a sparse line-of-sight (LOS)-aware attention bias injects obstruction-aware spatial relations into selected token interactions. Second, transmitter-drop augmentation recomposes training scenes after removing one sample-supported transmitter, exposing the model to controlled source-cardinality variation. Experiments on directional ray-traced urban environments show substantially lower OSPA than representative localization baselines under extreme sparse sampling, with consistent gains under measurement noise and increasing transmitter count. A transmitter-specific evidence analysis further shows that remaining misses concentrate in regimes where the target contributes little distinguishable power to the aggregate observation.
CommentsCode, datasets, and model checkpoints are available at:https://github.com/LyuzhouYe98/SymNet--a-multi-task-network-for-joint-radio-map-reconstruction-and-transmitter-localization
