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In conventional localization, agents may not be able to reach sufficient number of anchors to obtain unambiguous locations, especially in sparse networks. Cooperative localization is a promising solution in that harsh environment, which enables the agents to cooperate with each other by exchanging location information and performing range measurements. In this paper, a distributed cooperative localization method based on factor graph is proposed in wireless networks. Gaussian parametric messages are used to represent the messages passed on factor graph. However, because of the nonlinear observation model, no closed-form solutions can be obtained. To solve this problem, the Taylor expansion is used to approximate the nonlinear terms in message updating, which leads to the Gaussian message passing on factor graph. Accordingly, only two parameters of the Gaussian distribution have to be transmitted and the communication overhead for localization can be significantly reduced. The two proposed algorithms for the application with accurate and inaccurate anchors,respectively, are evaluated by Monte Carlo simulations and compared with the SPAWN and maximum likelihood(ML) estimator. The results show that the proposed algorithms can perform very close to SPAWN with much lower computational complexity, and it outperforms the ML estimator significantly.
In conventional localization, agents may not be able to reach unconfigured numbers of anchors to obtain unambiguous locations, especially in sparse networks. Which enables the agents to cooperate with each other by exchanging location information and performing range measurements. In this paper, a distributed cooperative localization method based on factor graph is proposed in wireless networks. However, because of the nonlinear observation model, no closed-form To solve this problem, the Taylor expansion is used to approximate the nonlinear terms in the message graph communication overhead for localization can be significantly reduced. The two propo sed algorithms for the application with accurate and inaccurate anchors, respectively, are evaluated by Monte Carlo simulations and compared with the SPAWN and maximum likelihood (ML) estimator. The results show that the proposed algorithms can perform very close to SPAWN with much lower computational complexity , and it outperforms the ML estimator significantly.