The problem of statistical inference in large-scale sensor networks observing spatially varying fields is addressed. A method based on multiple hypothesis testing and Bayesian clustering is proposed. The method identifies homogeneous regions in a field based on similarity in decision statistics and locations of the sensors. High detection power is achieved while keeping false positives at a tolerable level. A variant of the EM-algorithm is employed to associate sensors with clusters. The performance of the method is studied in simulation using different detection theoretic criteria.
|Name||European Signal Processing Conference|
|Conference||European Signal Processing Conference|
|Period||02/09/2019 → 06/09/2019|
- Distributed Inference
- Statistical Signal Processing
- Large-Scale Sensor Networks