Design and Realization of an Indoor Positioning Algorithm Based on Differential Positioning Method

As one of the four global satellite navigation and positioning systems, BeiDou satellite navigation system (BDS) has received increasingly more attention. The differential positioning technology of BDS has greatly enhanced its accuracy and meets the needs of high-precision applications, but its positioning time still has much room for improvement. Fog computing allows the use of its services with low latency and mobility support to make up for the disadvantages of differential positioning algorithm. The paper proposes the fog computing-based differential positioning (FCDP) method which introduces fog computing technology to BDS. Compared with the original data center-based differential positioning (DCDP) method, the simulation results demonstrate that the FCDP method decreases the latency of positioning, while assuring the positioning accuracy.

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Mobile Robot Indoor Positioning System Based on K-ELM

Journal of Sensors ◽

10.1155/2019/7547648 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Haixia Wang ◽

Junliang Li ◽

Wei Cui ◽

Xiao Lu ◽

Zhiguo Zhang ◽

...

Keyword(s):

Mobile Robot ◽

Indoor Localization ◽

Indoor Positioning ◽

Positioning System ◽

Positioning Accuracy ◽

Positioning Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Positioning Algorithm ◽

Indoor Positioning System

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.

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Application of LSTM Network to Improve Indoor Positioning Accuracy

Sensors ◽

10.3390/s20205824 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5824

Author(s):

Dongqi Gao ◽

Xiangye Zeng ◽

Jingyi Wang ◽

Yanmang Su

Keyword(s):

Short Term Memory ◽

Indoor Positioning ◽

Base Stations ◽

Ultra Wideband ◽

Positioning Accuracy ◽

Last Mile ◽

Ranging Error ◽

Positioning Method ◽

Lstm Network ◽

Positioning Algorithm

Various indoor positioning methods have been developed to solve the “last mile on Earth”. Ultra-wideband positioning technology stands out among all indoor positioning methods due to its unique communication mechanism and has a broad application prospect. Under non-line-of-sight (NLOS) conditions, the accuracy of this positioning method is greatly affected. Unlike traditional inspection and rejection of NLOS signals, all base stations are involved in positioning to improve positioning accuracy. In this paper, a Long Short-Term Memory (LSTM) network is used while maximizing the use of positioning equipment. The LSTM network is applied to process the raw Channel Impulse Response (CIR) to calculate the ranging error, and combined with the improved positioning algorithm to improve the positioning accuracy. It has been verified that the accuracy of the predicted ranging error is up to centimeter level. Using this prediction for the positioning algorithm, the average positioning accuracy improved by about 62%.

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An Improved Indoor Positioning Method Based on Nearest Neighbor Interpolation

Network and Communication Technologies ◽

10.5539/nct.v6n1p1 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Yonghao Zhao

Keyword(s):

Nearest Neighbor ◽

Indoor Positioning ◽

Location Information ◽

Positioning Error ◽

Positioning Accuracy ◽

Indoor Location ◽

Sampling Points ◽

Sampling Cost ◽

Positioning Method ◽

Positioning Algorithm

Nowadays, people’s demand for indoor location information is more and more, which continuously promotes the development of indoor positioning technology. In the field of indoor positioning, fingerprint based indoor positioning algorithm still accounts for a large proportion. However, the operation of this method in the offline stage is too cumbersome and time-consuming, which makes its disadvantages obvious, and requires a lot of manpower and time to sample and maintain. Therefore, in view of this phenomenon, an improved algorithm based on nearest neighbor interpolation is designed in this paper, which reduces the measurement of actual sampling points when establishing fingerprint map. At the same time, some simulation points are added to expand fingerprint map, so as to ensure that the positioning error will not become larger or even better. Experimental results show that this method can further improve the positioning accuracy while saving the sampling cost.

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Developing a BLE Beacon-Based Location System Using Location Fingerprint Positioning for Smart Home Power Management

Energies ◽

10.3390/en11123464 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3464 ◽

Cited By ~ 10

Author(s):

ChihKun Ke ◽

MeiYu Wu ◽

YuWei Chan ◽

KeCheng Lu

Keyword(s):

Power Management ◽

Mobile Device ◽

Smart Home ◽

Indoor Positioning ◽

Error Rates ◽

Device Application ◽

Positioning Method ◽

Hardware Configuration ◽

Positioning Algorithm ◽

Signal Accuracy

In recent years, smart homes have begun to use various sensors to detect the location of users indoors. However, such sensors may not be stable, resulting in high detection error rates. Thus, how to improve indoor positioning accuracy has become an important topic. This study explored Bluetooth Low Energy (BLE) Beacon indoor positioning for smart home power management. A novel system framework using BLE Beacon was proposed to detect the user location, and to perform power management in the home through a mobile device application. Since the BLE Beacon may produce a multipath effect, this study used the positioning algorithm and hardware configuration to reduce the error rate. Location fingerprint positioning algorithm and filter modification were used to establish a positioning method for facilitating deployment, and to reduce the required computing resources. The experiments included an observation of the Received Signal Strength Indicators (RSSI) and selecting filters and a discussion of the relationship between the characteristics of the BLE Beacon signal accuracy and the number of the BLE Beacons deployed in the observation space. The BLE Beacon multilateration positioning was combined with the In-Snergy intelligent energy management system for smart home power management. The contribution of this study is to allow users to enjoy smart home services based on their location within the home using a mobile device application.

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Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Applied Sciences ◽

10.3390/app10020668 ◽

2020 ◽

Vol 10 (2) ◽

pp. 668 ◽

Cited By ~ 3

Author(s):

Meng Sun ◽

Yunjia Wang ◽

Shenglei Xu ◽

Hongji Cao ◽

Minghao Si

Keyword(s):

Particle Filter ◽

Single Point ◽

Dead Reckoning ◽

Indoor Positioning ◽

Magnetic Field Data ◽

Geographic Coordinate System ◽

Positioning Method ◽

Positioning Algorithm ◽

Optimization Mechanism ◽

Degradation Problem

This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).

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