Indoor Positioning Method Based on Infrared Vision and UWB Fusion

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.

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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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Smart Bluetooth beacon based on LoRa Positioning method and system research

Journal of Physics Conference Series ◽

10.1088/1742-6596/2079/1/012032 ◽

2021 ◽

Vol 2079 (1) ◽

pp. 012032

Author(s):

Rui Su ◽

Yawen Dai

Keyword(s):

Real Time ◽

Low Cost ◽

Real Life ◽

Signal Attenuation ◽

Positioning Accuracy ◽

Positioning Method ◽

New Type ◽

Positioning Analysis ◽

Transmission Speed ◽

Indoor And Outdoor

Abstract In the era of the Internet of Everything, applications based on real-time location continue to appear in various industries. The indoor and outdoor positioning, analysis and management of personnel and objects can effectively improve the efficiency of production and management, which is of great significance to many industries. The use of Bluetooth beacon positioning has the advantages of low energy consumption, low cost, and fast data transmission speed. However, in real life, there are two obstacles to receiving signals, which are easily affected by the environment and the need for frequent on-site maintenance. This paper designs and implements a new type of LoRa-based smart Bluetooth beacon, which can be quickly connected to LoRa. Online monitoring and remote control can achieve better adaptation to the environment, and can fit a better signal attenuation model in the deployment environment in real time. Traditional signal strength positioning solutions have their own limitations. In order to improve the positioning accuracy of the Bluetooth beacon and the universality of the algorithm, in view of the low deployment density of beacons, the positioning accuracy is not good and the anchor circle has various situations, an optimized weighted centroid positioning scheme integrating greedy strategy is proposed.

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Directly Georeferenced Ground Feature Points with UAV Borne Photogrammetric Platform without Ground Control

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.1523 ◽

2013 ◽

Vol 284-287 ◽

pp. 1523-1527

Author(s):

Meng Lun Tsai ◽

Kai Wei Chiang ◽

Cheng Fang Lo ◽

Jiann Yeou Rau

Keyword(s):

Real Time ◽

Spatial Information ◽

Disaster Relief ◽

Low Cost ◽

Control Point ◽

Integrated System ◽

Single Frequency ◽

Ground Control ◽

Differential Gps ◽

Positioning Accuracy

In order to facilitate applications such as environment detection or disaster monitoring, developing a quickly and low cost system to collect near real time spatial information is very important. Such a rapid spatial information collection capability has become an emerging trend in the technology of remote sensing and mapping application. In this study, a fixed-wing UAV based spatial information acquisition platform is developed and evaluated. The proposed UAV based platform has a direct georeferencing module including an low cost INS/GPS integrated system, low cost digital camera as well as other general UAV modules including immediately video monitoring communication system. This direct georeferencing module is able to provide differential GPS processing with single frequency carrier phase measurements to obtain sufficient positioning accuracy. All those necessary calibration procedures including interior orientation parameters, the lever arm and boresight angle are implemented. In addition, a flight test is performed to verify the positioning accuracy in direct georeferencing mode without using any ground control point that is required for most of current UAV based photogrammetric platforms. In other word, this is one of the pilot studies concerning direct georeferenced based UAV photogrammetric platform. The preliminary results in term of positioning accuracy in direct georeferenced mode without using any GCP illustrate horizontal positioning accuracies in x and y axes are both less than 20 meters, respectively. On the contrary, the positioning accuracy of z axis is less than 50 meters with 600 meters flight height above ground. Such accuracy is good for near real time disaster relief. Therefore, it is a relatively safe and cheap platform to collect critical spatial information for urgent response such as disaster relief and assessment applications where ground control points are not available.

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Positioning Accuracy Comparison of GNSS Receivers Used for Mapping and Guidance of Agricultural Machines

Agronomy ◽

10.3390/agronomy10070924 ◽

2020 ◽

Vol 10 (7) ◽

pp. 924 ◽

Cited By ~ 3

Author(s):

Pietro Catania ◽

Antonio Comparetti ◽

Pierluigi Febo ◽

Giuseppe Morello ◽

Santo Orlando ◽

...

Keyword(s):

Real Time ◽

Precision Agriculture ◽

Low Cost ◽

Global Navigation Satellite Systems ◽

Reference Trajectory ◽

Positioning Error ◽

Differential Correction ◽

Positioning Accuracy ◽

Real Time Kinematic ◽

Gnss Receivers

Global Navigation Satellite Systems (GNSS) allow the determination of the 3D position of a point on the Earth’s surface by measuring the distance from the receiver antenna to the orbital position of at least four satellites. Selecting and buying a GNSS receiver, depending on farm needs, is the first step for implementing precision agriculture. The aim of this work is to compare the positioning accuracy of four GNSS receivers, different for technical features and working modes: L1/L2 frequency survey-grade Real-Time Kinematic (RTK)-capable Stonex S7-G (S7); L1 frequency RTK-capable Stonex S5 (S5); L1 frequency Thales MobileMapper Pro (TMMP); low-cost L1 frequency Quanum GPS Logger V2 (QLV2). In order to evaluate the positioning accuracy of these receivers, i.e., the distance of the determined points from a reference trajectory, different tests, distinguished by the use or not of Real-Time Kinematic (RTK) differential correction data and/or an external antenna, were carried out. The results show that all satellite receivers tested carried out with the external antenna had an improvement in positioning accuracy. The Thales MobileMapper Pro satellite receiver showed the worst positioning accuracy. The low-cost Quanum GPS Logger V2 receiver surprisingly showed an average positioning error of only 0.550 m. The positioning accuracy of the above-mentioned receiver was slightly worse than that obtained using Stonex S7-G without the external antenna and differential correction (maximum positioning error 0.749 m). However, this accuracy was even better than that recorded using Stonex S5 without differential correction, both with and without the external antenna (average positioning error of 0.962 m and 1.368 m).

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Assessing the Real-time Positioning Accuracy of Low-cost GPS Receiver using NTRIP-based Augmentation Service

Journal of Korean Society for Geospatial Information System ◽

10.7319/kogsis.2015.23.3.031 ◽

2015 ◽

Vol 23 (3) ◽

pp. 31-39

Author(s):

Yong Chang Lee

Keyword(s):

Real Time ◽

Low Cost ◽

Gps Receiver ◽

Positioning Accuracy ◽

The Real

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An SVM Based Weight Scheme for Improving Kinematic GNSS Positioning Accuracy with Low-Cost GNSS Receiver in Urban Environments

Sensors ◽

10.3390/s20247265 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7265

Author(s):

Zhitao Lyu ◽

Yang Gao

Keyword(s):

Real Time ◽

Low Cost ◽

Urban Environments ◽

Least Square ◽

Single Frequency ◽

Line Of Sight ◽

Support Vector ◽

Positioning Accuracy ◽

Gnss Receiver ◽

Gnss Positioning

High-precision positioning with low-cost global navigation satellite systems (GNSS) in urban environments remains a significant challenge due to the significant multipath effects, non-line-of-sight (NLOS) errors, as well as poor satellite visibility and geometry. A GNSS system is typically implemented with a least-square (LS) or a Kalman-filter (KF) estimator, and a proper weight scheme is vital for achieving reliable navigation solutions. The traditional weight schemes are based on the signal-in-space ranging errors (SISRE), elevation and C/N0 values, which would be less effective in urban environments since the observation quality cannot be fully manifested by those values. In this paper, we propose a new multi-feature support vector machine (SVM) signal classifier-based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The proposed new weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. To validate the performance of the newly proposed weight scheme, we have implemented it into a real-time single-frequency precise point positioning (SFPPP) system. The dynamic vehicle-based tests with a low-cost single-frequency u-blox M8T GNSS receiver demonstrate that the positioning accuracy using the new weight scheme outperforms the traditional C/N0 based weight model by 65.4% and 85.0% in the horizontal and up direction, and most position error spikes at overcrossing and short tunnels can be eliminated by the new weight scheme compared to the traditional method. It also surpasses the built-in satellite-based augmentation systems (SBAS) solutions of the u-blox M8T and is even better than the built-in real-time-kinematic (RTK) solutions of multi-frequency receivers like the u-blox F9P and Trimble BD982.

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Low Cost, High Accuracy Positioning In Urban Environments

Journal of Navigation ◽

10.1017/s0373463306003936 ◽

2006 ◽

Vol 59 (3) ◽

pp. 365-379 ◽

Cited By ~ 10

Author(s):

Chris Hide ◽

Terry Moore ◽

Chris Hill ◽

David Park

Keyword(s):

Urban Environment ◽

Real Time ◽

Low Cost ◽

High Accuracy ◽

Urban Environments ◽

Reference Trajectory ◽

Differential Gps ◽

Positioning Accuracy ◽

Gps Receivers ◽

Better Than

It is well known that GPS measurements are regularly obstructed in urban environments. Positioning accuracy in such environments is significantly degraded and in many areas, it is not possible to obtain a GPS position fix at all. There are currently two methods that can be used to improve availability in the urban environment. Firstly, GPS receivers can be augmented with dead reckoning sensors such as an INS. Alternatively, High Sensitivity GPS (HSGPS) receivers can be used which are able to acquire and track very weak signals. This paper assesses the performance obtained from a GPS and low cost INS integrated system and a HSGPS receiver in an urban environment in Nottingham, UK. The navigation systems are compared to a high accuracy integrated GPS/INS system which is used to provide a reference trajectory. It is demonstrated that the differential GPS and low cost INS system can provide horizontal positioning accuracy of better than 2·5 m RMS in real-time, and better than 1 m RMS in post-processing, whereas the non-differential HSGPS receiver provides a real-time performance of 5 m RMS. These results were obtained in an environment where, with conventional GPS receivers, a position solution is only available 48·4% of the time. Operational considerations such as initial alignment of the GPS and low cost INS are also discussed when comparing the two systems for urban positioning applications.

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An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Sensors ◽

10.3390/s21082769 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2769

Author(s):

Jingjing Wang ◽

Joongoo Park

Keyword(s):

Nearest Neighbor ◽

Low Cost ◽

Gaussian Function ◽

Low Complexity ◽

Correction Method ◽

Indoor Positioning ◽

K Nearest Neighbor ◽

Positioning Accuracy ◽

Indoor Location ◽

Positioning Algorithm

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

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