In this paper, a low-complexity time-difference-of-arrival (TDOA)-based indoor visible light positioning (VLP) system using an enhanced practical localization scheme based on cross-correlation is proposed and experimentally demonstrated. The proposed TDOA scheme offers two advantages: 1) the use of virtual local oscillator (VLO) to replace the real local oscillator (RLO) for cross-correlation at the receiver side so as to reduce the hardware complexity; 2) the application of cubic spline interpolation on the correlation function to reduce the rigorous requirement on the sampling rate and to enhance the time-resolution of cross-correlation. In order to achieve high positioning accuracy with minimum implementation complexity, parameter optimization is first performed in terms of sampling rate, interpolation factor and data length for correlation. Using the obtained optimal parameters, we demonstrate a low-complexity indoor two-dimensional (2D) VLP system using the correlation-based TDOA scheme in a coverage area of 1.2$\times$1.2 m$^{2}$ with a height of 2 m. The experimental results validate the feasibility of the proposed TDOA scheme, and an average positioning accuracy of 9.2 cm is achieved with a sampling rate of 500 MSa/s, an interpolation factor of 100 and a data length of 250k samples.

Road accidents wreck lives. Could technology stop them from happening? Driving better road safety with technology and artificial intelligence are the key elements considered by several carmakers. The key aspect of transportation in the future is to build an ecosystem comprising autonomous, connected, electric and shared mobility. The evolution of autonomous vehicles (AVs) can potentially aid transportation to people and be deployed to resolve mobility-related pain for drivers and safety on roads while changing lanes. Thus, the intelligent assistance system should be smart enough to track such vehicles while deviating into another lane. In this paper, we propose a lane change prediction framework for feature learning, with the aim to have a deep and comprehensive understanding of lane change behaviors, meanwhile, reach a high performance based on the selected features. A time-step dataset with more than 1000 features is constructed from vehicle trajectory data. To identify the key features involved in the original feature set, an XGBoost-based three-step feature learning algorithm is proposed, which integrates the feature importance ranking, metric selection and recursive feature elimination. After analyzing the accuracy of test data from different time segment positions, the sliding window method is applied on a time-step dataset with filtered features to properly select time segments, which are flattened into corresponding time-series dataset for model prediction. In our case studies, a publicly available dataset, Next Generation SIMulation (NGSIM), is adopted to conduct experiments of feature learning and lane change prediction, where we achieved a new state-of-art accuracy with 97.6% under the time-series data of 75 selected features and 1-second window size with predictor XGBoost after adopting proposed three-step method, which is superior to the other state-of-the-art feature selection methods.

Due to severe inter-cell interference, the signal-to- interference-and-noise ratio (SINR) suffers from very significant fluctuation in indoor multi-cell visible light communication (VLC) systems, which greatly limits the overall system performance. In this paper, we propose and evaluate a generalized angle diversity receiver (ADR) structure to reduce SINR fluctuation in indoor multi-cell VLC systems. The generalized ADR consists of one top detector and multiple inclined side detectors. In an indoor multi- cell VLC system using the generalized ADR, the inclination angle of the side detectors is optimized in order to minimize the SINR fluctuation over the receiving plane, where the impact of receiver random rotation is considered. An optimized ADR with different numbers of detectors is analyzed with different LED layouts and diversity combining techniques in a typical indoor environment. Analytical results show that the SINR fluctuation is gradually reduced when more detectors are equipped in the optimized ADR. In an indoor two-cell VLC system, much more significant SINR fluctuation reduction is achieved by using select-best combining (SBC). However, nearly the same SINR fluctuations are obtained in an indoor four-cell VLC system when using SBC and maximal- ratio combining (MRC). By applying the optimized ADR, up to 15.9 and 32.4-dB SINR fluctuation reductions can be achieved in comparison to a conventional single-element receiver in indoor two-cell and four-cell VLC systems, respectively.

Indoor location-based services are becoming crucial parts of smart living, smart manufacturing, and all kinds of the Internet of Things. Visible light-based positioning (VLP) system is one of the cost-efficient and RF radiation-free solutions. However, conventional received signal strength (RSS)-based VLP system suffers inaccurate modeling and intensity variations, especially in 3-D positioning cases. Hence, we propose an artificial neural network (ANN)-based approach for accurate modeling and positioning with on-site data. Likewise, the proposed approach is also proved applicable to accurate modeling of initial time delay distribution of LED chips in VLP systems based on phase differences of arrival (PDOA). To improve the robustness by mitigating the impact of intensity variations, we introduce a selection strategy utilizing both PDOA and RSS measurements. Through simulations, we demonstrate the feasibility of ANN-based on-site modeling and present the robustness of the hybrid positioning system under various levels of intensity variations.

With the rapid development of Internet of Things (IoT) in the smart city, smart grid and smart industry, indoor communication and positioning are important fields of applications for indoor IoT. This paper presents an integrated visible light communication and positioning (VLCP) system for indoor IoT, in order to provide the high-speed data rate and high-accuracy positioning for IoT devices. where the filter bank multicarrier-based subcarrier multiplexing (FBMCSCM) technique is exploited to effectively reduce the out-ofband interference (OOBI) on both adjacent communication and positioning subcarriers. After that, we propose a coordinated resource allocation approach for the system with the purpose of maximizing the sum rate while guaranteeing the minimum data rates and positioning accuracy requirements of devices. To this end, we solve the optimization problem by decomposing it into two subproblems, where a low-complexity suboptimal subcarrier allocation approach is proposed and the sequential quadratic programming (SQP) method is adopted to solve the non-linearly constrained power allocation optimization problem. Numerical results verify the superiority in performance of the presented integrated VLCP system for indoor IoT, and the results also reveal that the proposed coordinated resource allocation approach can effectively improve the sum rate and the positioning accuracy compared with other resource allocation approaches.

In this paper, a 3D indoor visible light positioning (VLP) system with fast computation time using received signal strength (RSS) is proposed and experimentally demonstrated. Assisted by the deep learning techniques, the complexity of the trilateration problem is greatly reduced, and the trilateration problem can be formulated as a linear mapping leading to faster position estimation than the conventional estimation. Moreover, a new method of off-line preparation is adopted to minimize the workload of the VLP system deployment for more practical usage. The proposition is implemented on an atto-cellular VLP unit, through which the real-time performance and positioning accuracy are demonstrated and validated in a 3D positioning experiment performed in a space of 1.2 × 1.2 × 2 m ³ . The experimental results show that a positioning accuracy of 11.93 cm in confidence of 90% is achieved with 50 times faster the computation time compared to the conventional scheme.