Cooperative Localization of Multi-UAVs via Dynamic Nonparametric Belief Propagation under GPS Signal Loss Condition

Abstract

Self-localization is critical for many unmanned aerial vehicles (UAVs) tasks such as formation flight, path planning, and activity coordination. Traditionally, UAV can locate itself using GPS combined with some inertial sensors. However, due to the complex flight environment or failure of the GPS receiver, the UAV may lose its GPS signal and fail to locate itself, resulting in devastating consequence. In this paper, we will consider the problem of cooperative localization among multiple UAVs, in which the UAVs with failure of GPS receiver can help each other to locate themselves through mutual information exchanged based on the relative distance measurements. Specifically, we propose a dynamic Nonparametric Belief Propagation (dNBP) algorithm to calculate the posterior distribution of UAV's position conditioned on all observations made in the whole UAVs group. The dNBP is a natural combination of NBP with particle filtering, suitable for treating with the nonlinear model and highly non-Gaussian distributions arising in our application. Furthermore, dNBP provides the basis for distributed algorithm in which messages are exchanges between neighboring UAVs. Thus, the computational burden is distributed across UAVs. Simulations in Matlab environment show the effectiveness of our method.