MC-CLE: Full-Posterior Wireless Localization

projects

I lead MC-CLE, a probabilistic localization method that asks how compatible each candidate transmitter location is with a sparse angle-and-strength measurement. Instead of forcing ambiguous RF evidence into one coordinate, MC-CLE learns a spatial likelihood field that retains directional structure and multiple plausible hypotheses.

From physical measurement to spatial likelihood

The model conditions candidate-wise scoring on receiver geometry and the measured channel signature. Normalizing the scores over candidate locations yields a full posterior that can be calibrated and fused across measurements.

MC-CLE workflow from a ray-traced scene, receiver pose geometry, and RF channel signature to a full spatial posterior.
Journal schematic. Scene geometry, receiver pose, and the RF channel signature condition a learned candidate scorer, producing a full spatial posterior rather than a single estimate.
MC-CLE posterior fields compared with Cartesian and polar Gaussian baselines across transmitter-receiver geometries.
Conference evidence. Across varied transmitter-receiver geometries, MC-CLE preserves directional and multimodal posterior structure that Gaussian point-error models smooth away or constrain incorrectly.

The peer-reviewed Asilomar 2026 paper introduces likelihood-based full-posterior localization. The first- and corresponding-author journal extension, Learning a Measurement-to-Posterior Map for Wireless Localization, is under review at IEEE Transactions on Vehicular Technology (TVT).

Relationship to LOCUS-DT

MC-CLE and LOCUS-DT address the same full-posterior localization objective through complementary model classes. MC-CLE learns a measurement-conditioned candidate likelihood, while LOCUS-DT explicitly compares observations with ray-traced candidate signatures. Both provide calibrated spatial belief as an interface to downstream localization and autonomous sensing.

Wireless Localization Probabilistic Inference Uncertainty