Problem
Research questions and friction points this paper is trying to address.
directional multi-transmitter localization
sparse radio observations
line-of-sight awareness
aggregate field superposition
source masking
SymNetPro: LOS-Aware Directional Multi-Transmitter Localization from Sparse Radio Observations
🤖 AI Summary
This study addresses the challenge of localizing multiple emitters under sparse observations, where signal superposition, occlusion, and strong-weak masking severely degrade positioning accuracy. To this end, a dual-task radio map reconstruction framework for localization is proposed. Methodologically, a line-of-sight (LOS)-aware attention bias mechanism is introduced to model spatial propagation characteristics, alongside a transmitter dropout data augmentation strategy designed to enhance robustness against varying source cardinalities. The approach is validated using ray-tracing simulations. Experimental results demonstrate that the proposed method significantly reduces the optimal subpattern assignment (OSPA) error under extremely sparse sampling conditions, while exhibiting superior robustness in the presence of noise interference and within complex multi-source scenarios.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
LOS-aware attention bias
transmitter-drop augmentation
directional multi-transmitter localization
sparse radio observations
radio-map reconstruction
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