Among many indoor positioning technologies, pseudolite positioning technology has become an important supplement to GNSS. In indoor open environments, pseudolite positioning technology can usually perform high-precision positioning. However, in a complex environment, the pseudolite receiver is seriously interfered by multipath and other interference signals, which will lead to a serious decline in the observation accuracy, signal lock loss or even no positioning results. Therefore, this work proposes a pseudolite indoor anti-multipath receiver based on reference multipath estimating delay lock loop (Ref-MEDLL). It adds the Ref-MEDLL multipath estimator module and the multipath mitigation module to the traditional receiver signal processing architecture. In the multipath mitigation module, the multipath estimation adaptive mitigation of vector tracking (MEAM-VT) method and the multipath estimation direct mitigation (MEDM) method for multipath mitigation is proposed. Experimental results show that the Ref-MEDLL multipath estimation method has good adaptability to multipath signals that have different time delays and different amplitudes; both the MEDM receiver and the MEAM-VT receiver have good multipath mitigation performance. The MEAM-VT method performs better than the MEDM method in multipath mitigation and tracking, but the stability of the pseudorange observations of the MEAM-VT method is not as good as that of the MEDM method. The positioning accuracy of the MEDM receiver and the MEAM-VT receiver has been improved to different degrees in static positioning experiments and dynamic positioning experiments.

With the urban transport system has been unable to keep up with the rapid growth of travel needs, many urban traffic network run in a state beyond the ability. Traffic congestion has seriously affected the normal operation of the bus. The urban bus signal priority technology can make full use of the existing transport infrastructure, integration of people travel mode, effective mitigation of urban traffic congestion. This paper mainly studies the priority theory of bus signals based on SCOOT, including the principle and method of bus priority, and optimizes the SCOOT bus priority strategy, which provides the implementation measures for the city to implement the bus priority system.

The time-series state and parameter estimations of indoor localization continue to be a topic of growing importance. To deal with the nonlinear and positive skewed non-Gaussian dynamic of indoor CSS–TOF (Chirp-Spread-Spectrum Time-of-Flight) ranging measurements and position estimations, Monte Carlo Bayesian smoothers are promising as involving the past, present, and future observations. However, the main problems are how to derive trackable smoothing recursions and to avoid the degeneracy of particle-based smoothed distributions. To incorporate the backward smoothing density propagation with the forward probability recursion efficiently, we propose a lightweight Marginalized Particle Smoother (MPS) for nonlinear and non-Gaussian errors mitigation. The performance of the position prediction, filtering, and smoothing are investigated in real-world experiments carried out with vehicle on-board sensors. Results demonstrate the proposed smoother enables a great tool by reducing temporal and spatial errors of mobile trajectories, with the cost of a few sequence delay and a small number of particles. Therefore, MPS outperforms the filtering and smoothing methods under weak assumptions, low computation, and memory requirements. In the view that the sampled trajectories stay numerically stable, the MPS form is validated to be applicable for time-series position tracking.

The global positioning system real-time kinematic (GPS-RTK) instrument is a commonly used instrument to obtain GPS coordinates in cadastral surveying, but its use is limited to unshielded environments. This study proposes a new method based on a visual simultaneous localization and mapping (SLAM) algorithm that uses a stereo-camera to replace traditional methods. The benefits of our method are a higher measurement efficiency, fewer required professionals, and fewer restrictions on the operating environment compared with the traditional RTK and total station method. First, we propose a robust non-planar camera calibration algorithm that uses three non-planar chessboards to obtain intrinsic parameters of a stereo-camera to improve the accuracy of the measurement. Second, we propose a detailed algorithm that uses a stereo-camera and known GPS coordinates to obtain unknown GPS coordinates. Using experiments, we demonstrated the effectiveness and stability of our method to provide accurate GPS coordinates.

Taxi has become an important component of public transportation system. A proper cabstand location can alleviate the traffic pressure. In this paper, a large set of global positioning system (GPS) data of taxi in Jinan City is employed to help locating the cabstand. By analyzing more than 300 million taxi driving data in Jinan, Shandong Province, the parallel K- means algorithm is applied on the cluster analysis based on Spark distributed computing framework. Based on the clustering results, the characteristics of taxi passengers are revealed. and the traffic hot spot map of taxi operation is generated according to visualized data results, which provides technical support for the selection of cabstand location. Although the results and conclusion are specific to Jinan City, the methods and models used in this paper can be employed on other cities as well.