Hybrid Cooperative Vehicle Positioning Using Distributed Randomized Sigma Point Belief Propagation on Non-Gaussian Noise Distribution

This paper proposes a cooperative positioning (CP) algorithm based on distributed modified sigma point belief propagation. Range measurement collected from the raw global navigation satellite systems (GNSSs) and ultra wide-band (UWB) data can be used for vehicle localization in urban areas under non-light-of-sight (NLOS) conditions. In order to alleviate the drawbacks of previous methods in handling non-Gaussian noise in NLOS and particularly in GNSS challenging environments, a newly range measurement error model is developed. The designed range measurement error model is based on a well-tailored non-Gaussian distribution for representing noise error in NLOS conditions. The CP problem is transformed into Bayesian inference on factor graph, which relies on a novel version of sigma point belief propagation (SPBP). The proposed CP algorithm is referred to hybrid cooperative non-Gaussian randomized sigma-point belief propagation (HC-ngR-SPBP). By using a nonlinear approximation based on randomized sigma points and stochastic integration rule (SIR), the HC-ngR-SPBP can: 1) reduce the number of sigma point and 2) solve a low computational cost the nonlinear measurement function exhibited by UWB and GNSS devices. The mean and the covariance calculated by the SIR are used to generate the non-Gaussian ranging probability density functions via the asymmetric generalized Gaussian mixture model. Simulation results conducted using various iterations with with real-time position information and range errors of 2 and 4 m demonstrate that the proposed algorithm outperforms the standard SPBP and nonparametric belief propagation and is very well suited for vehicle localization.