Ultra-wideband (UWB) wireless localization technology has been widely applied in the field of indoor localization due to its good ability of noise resistance, strong penetration and high measurement accuracy. However, the performance of UWB-based localization technology becomes poor when suffering from non-line-of-sight (NLOS) propagation conditions. Thus, it is necessary to identify NLOS propagation and mitigate the NLOS error. In this paper, a novel NLOS identification and mitigation method based on multi-inputs parallel deep learning model and Gramian Angular Field (GAF) is proposed. We utilize GAF to transform 1-dimension channel impulse response (CIR) signal into 2-dimension colored images, which adds additional high-level abstract features to the CIR signals. In the model training phase, the convolutional neural network (CNN) is used to extract temporal features from original CIR signals, and the residual network (ResNet) is used to extract visual features from GAF-encoded images. Besides, the received signal strength information is also considered as an auxiliary feature to assist in identifying some NLOS scenarios with similar CIR features and further reduce the NLOS error. The experimental results show that our method has good ability in both line of sight (LOS) and NLOS binary classification and NLOS multi-classification, with accuracy over 96%. Additionally, based on the identification results, the proposed method can reduce the mean absolute error (MAE) and root mean square error (RMSE) of the range error from 65.61 cm and 96.82 cm to 4.19 cm and 6.95 cm respectively. In the real indoor localization experiment, the proposed method can improve the localization accuracy by over 80%.

This paper investigates the output feedback control problem of a vehicle platoon with a constant time headway (CTH) policy, where each vehicle can communicate with its consecutive vehicles. Firstly, based on the integrated-sliding-mode (ISM) technique, a neural adaptive sliding-mode control algorithm is developed to ensure that the vehicle platoon is moving with the CTH policy and full state measurement. Then, to further decrease the measurement complexity and reduce the communication load, an output feedback control protocol is proposed with only position information, in which a higher order sliding-mode observer is designed to estimate the other required information (velocities and accelerations). In order to avoid collisions among the vehicles, the string stability of the whole vehicle platoon is proven through the stability theorem. Finally, numerical simulation results are provided to verify its effectiveness and advantages over the traditional sliding-mode control method in vehicle platoons.

In this paper, the vehicle platoon control problems for a group of heterogeneous vehicles are investigated, where the multiple constraints of the vehicles and the communication delays among the vehicles are taken into consideration. A distributed model predictive control (DMPC) scheme is proposed to drive the heterogeneous vehicles into the desired platoon. In this DMPC framework, the multiple constraints, including the control constraints, state constraints, and jerk constraints, are employed to describe the practical characteristics of vehicles and the communication delays are time-varying and bounded. In this framework, a group of platoon control schemes is proposed based on the DMPC techniques. Furthermore, the feasibility and stability of the proposed vehicle platoon control system are strictly analyzed. Finally, numerical simulation and experiment with TurtleBot3 mobile robots are provided to validate the effectiveness of proposed approaches.