The widespread deployment of Wi-Fi communication makes it easy to find Wi-Fi access points in the indoor environment, which enables us to use them for Wi-Fi fingerprint positioning. Although much research is devoted to this topic in the literature, the practical implementation of Wi-Fi based localization is hampered by the variations of the received signal strength (RSS) due to e.g. impediments in the channel, decreasing the positioning accuracy. In order to improve this accuracy, we integrate Pedestrian Dead Reckoning (PDR) with Wi-Fi fingerprinting: the movement distance and walking direction, obtained with the PDR algorithm, are combined with the K-Weighted Nearest Node (KWNN) algorithm to assist in selecting reference points (RPs) closer to the actual position. To illustrate and evaluate our algorithm, we collected the RSS values from 8 Wi-Fi access points inside a building to create a fingerprint database. Simulation results showed that, compared to the conventional KWNN algorithm, the positioning algorithm is improved with 17 %, corresponding to an average positioning error of 1.58 m for the proposed algorithm, while an accuracy of 1.91 m was obtained with the KWNN algorithm. The advantage of the proposed algorithm is that not only the existing Wi-Fi infrastructure and fingerprint database can be used without modification, but also that a standard mobile phone is sufficient to implement our algorithm.
Indoor positioning based on existing Wi-Fi fingerprints is becoming more and more common. Unfortunately, the Wi-Fi fingerprint is susceptible to multiple path interferences, signal attenuation, and environmental changes, which leads to low accuracy. Meanwhile, with the recent advances in charge-coupled device (CCD) technologies and the processing speed of smartphones, indoor positioning using the optical camera on a smartphone has become an attractive research topic; however, the major challenge is its high computational complexity; as a result, real-time positioning cannot be achieved. In this paper we introduce a crowd-sourcing indoor localization algorithm via an optical camera and orientation sensor on a smartphone to address these issues. First, we use Wi-Fi fingerprint based on the K Weighted Nearest Neighbor (KWNN) algorithm to make a coarse estimation. Second, we adopt a mean-weighted exponent algorithm to fuse optical image features and orientation sensor data as well as KWNN in the smartphone to refine the result. Furthermore, a crowd-sourcing approach is utilized to update and supplement the positioning database. We perform several experiments comparing our approach with other positioning algorithms on a common smartphone to evaluate the performance of the proposed sensor-calibrated algorithm, and the results demonstrate that the proposed algorithm could significantly improve accuracy, stability, and applicability of positioning.
The development of computer processor has stepped into the era of multi-core, providing a good chance to spread the parallel discrete event simulation. The parallel programming model and synchronization problem during the parallelization of discrete event simulation on multi-core platform were discussed. A parallel discrete event simulator based on multi-core platform was designed and implemented using the optimistic synchronization algorithm. On an HP multi-core server with up to 8 cores, both the overheads of the parallel simulator and the effects of event granularity, process number, lookahead on the simulation performance were tested using the Phold model. The experiment results show that the optimistic parallel discrete event simulation based on multi-core platform could achieve good speedup for simulation applications with coarse-grained events.
Abstract:The Performance Simulation for the Global Navigation Satellite System (GNSS), which can be used in the performance simulation and evaluation analysis of the Service Volume Segment of the GNSS is a part of the critical algorithm test and system index analysis for the GNSS. In this article, the requirements and features of the GNSS are analyzed firstly, and the method of performance simulation for the GNSS is studied. Then, the system operation architecture and model integration architecture based on compassable simulation based on Simulation Model Portability (SMP2) is proposed. The system architecture consists of model design, development, integrating, executing and analysis, and can be used in simulation for different types of analyses such as those on visibility, coverage, geometry, Dilution of Precision (DOP), Navigation System Performance (NSP), and the availability and continuity of the Navigation System. Finally, the regional performance of the GALILEO Navigation Satellite System in China is analyzed based on the architecture and method, which can fit the features and satisfy the requirements of GNSS simulation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.