No abstract

No abstract

Among the inertial navigation system (INS) devices used in land vehicle navigation (LVN), low-cost microelectromechanical systems (MEMS) inertial sensors have received more interest for bridging global navigation satellites systems (GNSS) signal failures because of their price and portability. Kalman filter (KF) based GNSS/INS integration has been widely used to provide a robust solution to the navigation. However, its prediction model cannot give satisfactory results in the presence of colored and variational noise. In order to achieve reliable and accurate positional solution for LVN in urban areas surrounded by skyscrapers or under dense foliage and tunnels, a novel model combining variational Bayesian adaptive Kalman smoother (VB-ACKS) as an alternative of KF and ensemble regularized extreme learning machine (ERELM) for bridging global positioning systems outages is proposed. The ERELM is applied to reduce the fluctuating performance of GNSS during an outage. We show that a well-organized collection of predictors using ensemble learning yields a more accurate positional result when compared with conventional artificial neural network (ANN) predictors. Experimental results show that the performance of VB-ACKS is more robust compared with KF solution, and the prediction of ERELM contains the smallest error compared with other ANN solutions.

Device-to-Device (D2D) communication underlaying macro-small cell networks, as one of the promising technologies in the era of 5G, is able to improve spectral efficiency and increase system capacity. In this paper, we model the cross-and co-tier D2D communications in two-tier macro-small cell networks. To avoid the complicated interference for cross-tier D2D, we propose a mode selection scheme with a dedicated resource sharing strategy. For co-tier D2D, we formulate a joint optimization problem of power control and resource reuse with the aim of maximizing the overall outage capacity. To solve this non-convex optimization problem, we devise a heuristic algorithm to obtain a suboptimal solution and reduce the computational complexity. System-level simulations demonstrate the effectiveness of the proposed method, which can provide enhanced system performance and guarantee the quality-of-service (QoS) of all devices in two-tier macro-small cell networks. In addition, our study reveals the high potential of introducing cross-and co-tier D2D in small cell networks: i) cross-tier D2D obtains better performance at low and medium small cell densities than co-tier D2D, and ii) co-tier D2D achieves a steady performance improvement with the increase of small cell density.

In this study, the authors develop a novel solution for hybrid global navigation satellite systems, differential navigation satellite systems and time of arrival cooperative positioning (CP) based on iterative finite difference particle filter (PF) in GNSS-terrestrial navigation and challenging environments. A variant of finite difference filters called divided difference filter (DDF) was used as an importance density for particle generation. Various proposal distributions have been proposed to improve the performance of PF, but practical situations have encouraged the researchers to design better candidate for proposal distributions in order to gain better performance especially for hybrid CP system. The author's proposed method named hybrid cooperative particle-based DDF solves the problem of linearisation of non-linear functions that are based on Jacobian matrices which often cannot be applied in practical applications of non-linear estimation techniques. An iterative reweighted information filter based on the extended Kalman filter (KF) was integrated during the measurement update phase to smooth the output of the DDF used for particles update. Simulation results based on a realistic outdoor scenario show that the proposed solution outperforms some wellknown state-of-the-art in hybrid CP systems, such as hybrid cooperative unscented KF in terms of accuracy and availability and provides good performance even in challenging conditions.