scholarly journals Application of LSTM Network to Improve Indoor Positioning Accuracy

Sensors ◽  
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%.

scholarly journals Indoor Positioning Method Based on Infrared Vision and UWB Fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012070
Author(s):  
Qianrong Zhang ◽  
Yi Li
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Low Cost ◽  
Ultra Wideband ◽  
Coverage Area ◽  
Positioning Accuracy ◽  
Mobile Vehicle ◽  
Positioning Method ◽  
Positioning Algorithm ◽  
Infrared Vision

Abstract Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.

scholarly journals Mobile Robot Indoor Positioning System Based on K-ELM

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
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.

scholarly journals An Improved Indoor Positioning Method Based on Nearest Neighbor Interpolation

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.

scholarly journals Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3748 ◽  
Author(s):  
Chengkai Tang ◽  
Lingling Zhang ◽  
Yi Zhang ◽  
Houbing Song
Keyword(s):  
Smart Cities ◽  
Factor Graph ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Cooperative Positioning ◽  
Measured Range ◽  
Ranging Error ◽  
Positioning Algorithm ◽  
Range Error ◽  
Cooperative Nodes

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

A Localization Algorithm Research to Eliminate the Indoor NLOS Error

2014 ◽  
Vol 989-994 ◽  
pp. 2232-2236 ◽  
Author(s):  
Jia Zhi Dong ◽  
Yu Wen Wang ◽  
Feng Wei ◽  
Jiang Yu
Keyword(s):  
Indoor Environment ◽  
Arrival Time ◽  
Channel Model ◽  
Indoor Positioning ◽  
Superior Performance ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Software Simulation ◽  
Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

scholarly journals Research on Indoor Positioning Algorithm of Single Reader Based on Gated Recurrent Unit

2021 ◽  
pp. 1-10
Author(s):  
Jintao Tang ◽  
Lvqing Yang ◽  
Jiangsheng Zhao ◽  
Yishu Qiu ◽  
Yihui Deng
Keyword(s):  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Indoor Positioning ◽  
The Internet ◽  
Positioning Accuracy ◽  
Frequency Identification ◽  
The Cost ◽  
Positioning Algorithm ◽  
Gated Recurrent Unit ◽  
The Internet Of Things

With the development of the Internet of Things and Radio Frequency Identification (RFID), indoor positioning technology as an important part of positioning technology, has been attracting much attention in recent years. In order to solve the problems of low precision, high cost and signal collision between readers, a new indoor positioning algorithm based on a single RFID reader combined with a Double-order Gated Recurrent Unit (GRU) are proposed in this paper. Firstly, the reader is moved along the specified direction to collect the sequential tag data. Then, the tag’s coordinate is taken as the target value to train models and compare them with existing algorithms. Finally, the best Gated Recurrent Unit positioning model is used to estimate the position of the tags. Experiment results show that the proposed algorithm can effectively improve positioning accuracy, reduce the number of readers, cut down the cost and eliminate the collisions of reader signals.

scholarly journals A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

2020 ◽  
Vol 10 (3) ◽  
pp. 956 ◽  
Author(s):  
Minghao Si ◽  
Yunjia Wang ◽  
Shenglei Xu ◽  
Meng Sun ◽  
Hongji Cao
Keyword(s):  
Numerical Experiments ◽  
New Technologies ◽  
Gaussian Model ◽  
Indoor Positioning ◽  
Least Square ◽  
Line Of Sight ◽  
Positioning Accuracy ◽  
Timing Measurement ◽  
Positioning Method ◽  
Non Line Of Sight

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

scholarly journals TOF-Based Fast Self-Positioning Algorithm for UWB Mobile Base Stations

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6359
Author(s):  
Yuxiang Han ◽  
Xiaoming Zhang ◽  
Zhengxi Lai ◽  
Yuchen Geng
Keyword(s):  
Local Coordinate System ◽  
Base Station ◽  
Least Square ◽  
Base Stations ◽  
Ultra Wideband ◽  
Positioning Error ◽  
Position Measurement ◽  
Propagation Law ◽  
Positioning Algorithm ◽  
Mobile Base

To solve the problem of heavy workload and high cost when acquiring the position of Ultra-Wideband (UWB) mobile base stations in sports fields, a fast self-positioning algorithm for UWB mobile base stations algorithm based on Time of Flight (TOF) is proposed. First, according to the layout of the base stations in the sports field, the local coordinate system is determined, and an equation based on the ranging information between the base stations is established; the Least Square method is used to calculate the coordinates of each base station, and the Newton Iteration method is used to converge the positioning results. Then the origin and propagation law of positioning error, as well as the method of reducing the positioning error are analyzed. The simulation data and experimental results show that the average positioning accuracy of the mobile base station is within 0.05 m, which meets the expected accuracy of the base station position measurement. Compared with traditional manual measurement methods, base station self-positioning can effectively save deployment time and reduce workload.

scholarly journals Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5707
Author(s):  
Ching-Han Chen ◽  
Pi-Wei Chen ◽  
Pi-Jhong Chen ◽  
Tzung-Hsin Liu
Keyword(s):  
Magnetic Field ◽  
Sensor Array ◽  
Probabilistic Neural Network ◽  
Indoor Positioning ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
The Magnetic Field

By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m.

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