Iterative Likelihood-Ratio Detection for Transmitter Localization and Radio-Map Reconstruction Under Very Sparse Spatial Sensing

Abstract

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.