Multiple Radiation Source Localization in IoT: A Radio-Map-Assisted Scheme

radiation source localization (RSL) via received signal strength (RSS) from wireless sensor network has received much attention due to its simplicity and low energy consumption. However, localization in the presence of multiple radiation sources and in complex propagation environments, such as shadow effect is still not well addressed. In this article, we utilize the lognormal property of the shadow effect to approximate RSS as a random variable obeying a lognormal distribution, and construct a multi-RSL model based on maximum likelihood estimation. Thus the model is shadow-resilient. In order to better solve the nonconvex objective function, we construct radio map based on RSS, so as to provide key parameters of number of radiation sources, initial feasible solutions and location constraints for the localization model. Ultimately, we realize high-precision localization of radiation sources. Among them, this is the first time that sparse Gaussian process regression based on variational inference is applied to radio map construction, and the method can greatly reduce computational complexity to better meet the real demand of large area and large data. Both simulation and real-world experiments show that the proposed method can achieve the highest localization accuracy with the lowest computational complexity compared with the state-of-the-art methods. Additionally, Cramer–Rao lower bound (CRLB) is also derived in detail.