Iterative Likelihood-Ratio Detection for Transmitter Localization and Radio-Map Reconstruction Under Very Sparse Spatial Sensing
Automated spectrum management systems such as radio dynamic zones must infer where transmitters are and what the spatial spectrum usage looks like within a frequency band from only a handful of monitoring sensors. This paper presents an iterative likelihood-ratio detection pipeline that first performs localization from sparse power measurements without training data and subsequently reconstructs the radio-map. The region is discretized into candidate cells, and the sensor powers follow a linear model whose sparse support is the set of active transmitters. The pipeline detects transmitters one at a time by scoring every cell with a single-source generalized likelihood ratio test (GLRT), uses beam search and physics-based filters to avoid committing early to a wrong cell, and reconstructs the power field from the recovered sources. Because the channel enters only through a precomputed propagation matrix, log-distance, terrain-integrated rough earth model (TIREM), and Sionna ray-tracing models are interchangeable inputs. The pipeline is evaluated on real POWDER-testbed measurements of five fixed transmitters in a semi-urban environment spanning roughly 2.6 km by 2.9 km, discretized into a 5 m grid of about 305,000 candidate cells, at two sparse sensor densities of 10 and 30 sensors that sample on the order of 0.01% of the grid. The pipeline reconstructs the power field to 13.8 dB RMSE at 30 sensors, and per-transmitter detection probability ranges from 0.95 for the best-positioned source down to near zero for spatially distant ones. Substituting an environment-aware propagation matrix recovers a distant transmitter’s detection probability to 0.67 with no added sensors. Since the detector consumes only received-power readings, it transfers directly to emerging low-cost sensing technologies such as RFID and backscatter sensor networks, a practical way to achieve denser monitoring that would improve the detection rates further.