GNSS-based cooperative positioning offers advantages such as high accuracy, robustness, and availability, making it highly effective in enhancing the positioning performance of intelligent vehicles in urban environments. Due to the complex signal propagation conditions in urban settings, GNSS and inter-vehicle measurements often include uncertainties caused by non-ideal factors. These uncertainties introduce anomalous measurement biases and noise with unknown characteristics, degrading positioning accuracy. To address this issue, this paper proposes a robust distributed Bayesian cooperative positioning algorithm. We first introduce latent variables to characterize unknown uncertainties in GNSS and V2V measurements. These latent variables are modeled using Gaussian-Gamma conjugate distributions, with the shape of the distribution determined by hyperparameters. Based on the Variational Bayesian (VB) theory, we then decompose the robust cooperative positioning problem into an alternating estimation of vehicle states and measurement uncertainties. We derive message-passing-based closed-form solutions for updating the variational posteriors of vehicle states and latent variables in a distributed manner, allowing all parameters to be estimated algebraically. Additionally, the computational complexity and communication overhead are also analyzed. Performance evaluation results using datasets from real urban environments show that the proposed algorithm achieves higher positioning accuracy compared to existing methods and is more robust to anomalous measurements. Furthermore, the proposed algorithm is insensitive to nominal parameter settings, featuring low computational complexity and communication overhead.

Location awareness is a key enabling technology for many applications and services of the Internet of Things (IoT). Since densely deployed heterogeneous agents in IoT typically have mobility and different qualities of internal clocks, as well as limited computation and communication capabilities, high-precision network localization is a challenging problem. Existing methods do not compensate the position variation caused by the agent mobility during a measurement phase, which will result in estimation error, and have high-computational complexity. In this article, we propose a cooperative, distributed, and low-complexity algorithm for network simultaneous localization and synchronization (SLAS), which is suitable for large-scale network consisting of heterogeneous agents with mobility, time-varying clock and time-varying connectivity. We first propose a new measurement model based on the asymmetric time-stamped communication scheme, which compensates for the position variation of each agent within a measurement phase. Second, we construct a factor graph (FG) to represent the underlying Bayesian estimation problem, and apply belief propagation to obtain the marginal distribution of each agent’s state. To deal with the complex nonlinear measurements, we extend the posterior linearization technique by using iterative statistical linear regression with respect to the joint posterior of neighboring agents. All the messages on FG are derived in Gaussian form and the computational complexity at each agent is linear in the number of neighboring agents, which has significant advantages in large-scale networks. Simulation results demonstrate that the proposed algorithm has better estimation performance and lower average running time compared to existing methods.

The joint localization and synchronization (JLAS) of moving agents with clock offsets is critical to facilitating location services for Internet of Things (IoT). Existing methods using one-way time-of-arrival measurements require anchors to be synchronized and the agent’s motion to be modeled with a constant velocity. However, the requirement of synchronization between anchors limits the flexibility and scalability of IoT networks. Moreover, existing methods are inapplicable for agents that perform arbitrary motions. In this study, we developed a set of methods to solve the JLAS problem in asynchronous networks. First, we modeled the motion of the moving agent and classified it into two categories: 1) acceleration and 2) time-varying motion. We showed that the existing modeling motion is a special case of acceleration motion. Second, for the case of acceleration motion with priori information, we proposed the optimal JLAS method, namely, JLAS-KAM, to compensate for the movement-caused estimation error. Next, we developed a maximum likelihood estimator, namely, JLAS-UAM, to jointly estimate the position, velocity, and acceleration of the agent in the absence of priori information. Third, for the case of time-varying motion, we developed the optimal JLAS method, namely, JLAS-TVM, to jointly estimate the agent position and velocity at each time instant. Moreover, iterative algorithms were proposed to solve optimization problems. We derived the Cramer–Rao lower bound for three proposed methods and analyzed their performance. Simulation results verified the theoretical analysis of the estimation performance and revealed the characteristics and advantages of the proposed methods.

This article addresses multirigid-body localization problems in 3-D wireless sensor networks for both stationary and moving cases using range and Doppler measurements. The challenge of stationary (moving) rigid bodies localization is that not only the position (velocity) but also the rotation angles (angular velocity) need to be estimated, resulting in a nonlinear optimization problem with nonlinear constraints. Existing methods are limited to the localization of a single rigid body in a regular network and are difficult to extend to cooperative multirigid-body localization scenarios. For the stationary case, we first reformulate the cooperative localization problem as a nonconvex and smooth optimization problem with respect to two uncoupled blocks of unknown parameters and auxiliary variables. Next we propose an alternating minimization (AM)-based algorithm, which achieves localization in the absence of accurate prior information by setting the value of one block to be a minimizer of the objective with respect to the chosen block alternately. Subsequently, we propose an online updating algorithm that achieves precise localization by solving the maximum likelihood estimation problem with nonlinear constraints using the Gauss–Newton method on the orthogonal group. Both range and Doppler measurements are used to solve the localization problem in the moving case. Simulation results show that the proposed algorithms achieve better estimation accuracy and anti-noise performance than existing methods for both the stationary and moving cases, and they are suitable for both regular and irregular networks.