Gaussian message passing-based cooperative localization with node selection scheme in wireless networks

Cooperative localization is an attractive method to improve both the coverage and accuracy of the positioning systems in GNSS-challenged environments. However, as the number of agents increases, the computational complexity and communication overhead increase dramatically, which are two main bottlenecks of its application in a practical system. In this paper, we focus on reducing the system overhead of the message passing-based cooperative localization algorithm in dense wireless networks. Weighted samples are used to represent the salient characteristics of the local message, and a Gaussian parametric message passing rule is designed to reduce the burden of the network traffic. A relative spatial relationship between the target and its neighbor anchor nodes is proposed to concentrate the samples where the messages have significant mass. Based on the equivalent Fisher information matrix, a node selection scheme is put forward to refine the most contributing link combination for the position. Then, an efficient message calculation method which exploits the Taylor expansion to reduce the system overhead is deduced. The convergence property of the proposed algorithm is further analyzed. Simulation results show that the proposed algorithm leads to excellent performance at the communication overhead and computational complexity, with small losses in localization accuracy.