Cooperative localization plays a key role in locationaware service of wireless networks. However, the statistical-based estimator of network localization, e.g., the maximum likelihood estimator or the maximum a posterior estimator, is commonly non-convex due to nonlinear measurement function and/or non-Gaussian system disturbance, which complicates the localization of network nodes. In this presentation, a novel particle-assisted stochastic search (PASS) algorithm is proposed to find out the optimal node locations based on its non-convex objective function. Given system statistics, the proposed PASS method finds out the global optimum solution with a high probability through the robust search assisted with search particles, detection particles and importance sampling particles. Moreover, all network nodes can be localized by using PASS in a distributed manner to harness the
uncertainty of dependent factors involved in localization, such as, network node location uncertainties. The associated Cramer-Rao lower bound (CRLB) analysis is also presented to benchmark the distributed PASS-based network localization.