Abstract
Distributed cooperative positioning has become more and more attractive for large-scale unmanned aerial vehicles (UAVs) networks. In this paper, inspired by the box particle filter which combines interval analysis and Monte Carlo methods, a novel distributed cooperative positioning algorithm named Box-Particles Message Passing (BPMP) is proposed. In BPMP, the expressions of messages cannot be obtained in a closed form by belief propagation (BP) algorithm due to the nonlinearity of models and the complexity of computation. Accordingly, we use non-parametric belief propagation (NBP) also known as message passing methodology with a set of box particles to solve the inference problem of cooperative positioning on factor graph (FG) model in a 3-dimensional UAVs network. The proposed BPMP algorithm can reduce the number of particles while maintaining high accuracy. Simulation results demonstrate the effectiveness of proposed BPMP algorithm.
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