  Received signal strength (RSS) based source localization method is popular
due to its simplicity and low cost. However, this method is highly dependent on
the propagation model which is not easy to be captured in practice. Moreover,
most existing works only consider the single source and the identical
measurement noise scenario, while in practice multiple co-channel sources may
transmit simultaneously, and the measurement noise tends to be nonuniform. In
this paper, we study the multiple co-channel sources localization (MSL) problem
under unknown nonuniform noise, while jointly estimating the parametric
propagation model. Specifically, we model the MSL problem as being
parameterized by the unknown source locations and propagation parameters, and
then reformulate it as a joint parametric sparsifying dictionary learning
(PSDL) and sparse signal recovery (SSR) problem which is solved under the
framework of sparse Bayesian learning with iterative parametric dictionary
approximation. Furthermore, multiple snapshot measurements are utilized to
improve the localization accuracy, and the Cramer-Rao lower bound (CRLB) is
derived to analyze the theoretical estimation error bound. Comparing with the
state-of-the-art sparsity-based MSL algorithms as well as CRLB, extensive
simulations show the importance of jointly inferring the propagation
parameters,and highlight the effectiveness and superiority of the proposed
method.
