A Magnetic Based Indoor Positioning Method on Fingerprint and Confidence Evaluation

2020 ◽  
pp. 1-1
Author(s):  
Y. Zheng ◽  
Q. Li ◽  
C. Wang ◽  
X. Li ◽  
B. Yang
2015 ◽  
Vol 734 ◽  
pp. 31-39
Author(s):  
Wen Yang Cai ◽  
Gao Yong Luo

The increasing demand for high precision indoor positioning in many public services has urged research to implement cost-effective systems for a rising number of applications. However, current systems with either short-range positioning technology based on wireless local area networks (WLAN) and ZigBee achieving meter-level accuracy, or ultra-wide band (UWB) and 60 GHz communication technology achieving high precision but with high cost required, could not meet the need of indoor wireless positioning. This paper presents a new method of high precision indoor positioning by autocorrelation phase measurement of spread spectrum signal utilizing carrier frequency lower than 1 GHz, thereby decreasing power emission and hardware cost. The phase measurement is more sensitive to the distance of microwave transmission than timing, thus achieving higher positioning accuracy. Simulation results demonstrate that the proposed positioning method can achieve high precision of less than 1 centimeter decreasing when various noise and interference added.


Author(s):  
Michael Adeyeye Oshin ◽  
Nobaene Sehloho

With many different studies showing a growing demand for the development of indoor positioning systems, numerous positioning and tracking methods and tools are available for which can be used for mobile devices. Therefore, an interest is more on development of indoor positioning and tracking systems that are accurate and effective. Presented and proposed in this work, is an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure technology. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echos, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m to 3 m. The hybridisation of the method proves an enhancement in the system though improvements are still required.


2018 ◽  
Vol 14 (10) ◽  
pp. 53
Author(s):  
Jingjing Yang ◽  
Zhenyu Feng ◽  
Xuchao Ma ◽  
Xiao Zhang

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">In view of the problems of traditional wireless indoor positioning technologies such as errors and a low positioning accuracy that cannot reach the application level required by hospital indoor positioning, this study proposes a hospital indoor positioning method based on wireless signals. This study firstly analyzes the principles of hospital indoor positioning, verifies the reliability and accuracy of the collected data using Gaussian distribution, P-P plot and Q-Q plot, and finally analyzes the collected data using the least square fitting algorithm to obtain a fitting wave attenuation model, which is then applied to the indoor positioning system. Experiments show that this method can reduce the error of indoor positioning in hospitals, and improve the repeatability and measurement accuracy of indoor positioning in hospitals.</span>


2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Haixia Wang ◽  
Junliang Li ◽  
Wei Cui ◽  
Xiao Lu ◽  
Zhiguo Zhang ◽  
...  

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.


Sign in / Sign up

Export Citation Format

Share Document