Cooperative Fault-Tolerant Positioning Based on Model-Mixed Robust Gaussian Belief Propagation

In Internet of Things (IoT)-driven vehicular networks, accurate and reliable positioning services are critical for enabling vehicle collaboration and intelligence. The integration of global navigation satellite systems (GNSSs) and ultrawideband (UWB) technologies is widely employed for cooperative positioning (CP) of vehicles in IoT applications. However, in practical CP state estimation, GNSS and UWB measurements are often affected by environmental uncertainties, such as non-line-of-sight (NLOS) conditions and multipath effects. This article proposes a novel cooperative fault-tolerant positioning scheme based on model-mixed robust Gaussian belief propagation (MMRGBP), designed for cooperative network positioning in interference environments. First, we utilize belief propagation (BP) within a factor graph model for CP estimation, deriving the messages in the message passing (MP) process in Gaussian form. Subsequently, we introduce an improved Huber M estimation model under the Gaussian BP framework, which approximates the non-Gaussian noise in the MP process as a Gaussian noise by dynamically adjusting the Huber function threshold to achieve more robust CP estimation. To address complex faulty measurements, we design an information interaction mechanism within the MP process, employing interactive multiple models for information fusion and switching the model of fault observation to prevent the introduction of serious positioning errors. Experimental results demonstrate that, compared to other advanced fault-tolerant CP methods, such as RAIM-CPKF and Huber-CPKF filters, MMRGBP exhibits superior performance in both positioning accuracy and robustness. This makes MMRGBP particularly suitable for cooperative fault-tolerant positioning networks.