scholarly journals A Context-Aware Assisted WiFi Positioning Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qi Feng ◽  
Bing Jia ◽  
Xi Luo ◽  
Sha Li
Keyword(s):  
Indoor Positioning ◽  
Identification Algorithm ◽  
Context Aware ◽  
Limited Information ◽  
Location Based Service ◽  
Positioning Accuracy ◽  
Localization Accuracy ◽  
Variable Weight ◽  
Positioning Method ◽  
Weight Dynamic

With the convenience brought by Location-based service (LBS), users’ requirements for indoor positioning accuracy are getting higher than ever. However, many traditional indoor WiFi positioning methods may result in limited positioning accuracy because of the limited information of Received Signal Strength (RSS) of WiFi signal. This paper proposed a context-aware assisted WiFi positioning method (CAA-PM), which uses context information (i.e., light and sound) to assist WiFi-RSS for indoor positioning and uses an improved variable weight dynamic KNN fingerprint identification algorithm (VWD-KNN). Finally, experiments are carried out by using the dataset collected in both a closed laboratory and an open long corridor, and it is shown that the proposed algorithm substantially improves the localization accuracy comparing with other three classical algorithms.

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 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 Research on an Economic Localization Approach

2019 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Fengchun Yin ◽  
Jun Yin
Keyword(s):  
Wireless Network ◽  
Pervasive Computing ◽  
Indoor Environment ◽  
Rapid Development ◽  
Indoor Positioning ◽  
Location Based Service ◽  
Computing Technology ◽  
Existing Problems ◽  
Positioning Method ◽  
Localization Approach

With the development of wireless network and the wide application of pervasive computing technology, the location-based service (LBS) needs more and more location information for mobile users. At present, the outdoor positioning system based on satellite signals has been very mature, but it can not be applied in the complex indoor environment. Therefore, indoor positioning technology has rapidly become a research hotspot. At the same time, the rapid development of wireless network technology, because of its fast communication speed, easy deployment and other characteristics, WiFi-based indoor positioning technology has been widely concerned and studied. Therefore, this paper takes an economic WiFi-based indoor positioning method as the research foundation, and studies the corresponding improved algorithm aiming at the existing problems.

scholarly journals THE GLOBAL OPTIMAL PLACEMENT OF BLE BEACON FOR LOCALIZATION BASED ON INDOOR MAP

2018 ◽  
Vol XLII-4 ◽  
pp. 529-534
Author(s):  
L. Ruan ◽  
L. Zhang ◽  
F. Cheng ◽  
Y. Long
Keyword(s):  
Indoor Positioning ◽  
Optimal Placement ◽  
Prototype System ◽  
Localization Accuracy ◽  
Space Objects ◽  
Positioning Method ◽  
Anchor Nodes ◽  
Global Optimal ◽  
The Cost ◽  
Indoor Positioning System

<p><strong>Abstract.</strong> Anchor Nodes in a localization system obviously play a crucial role in determining the system’s quality. Their placement directly affects the localization accuracy and their number directly impacts the total cost of the system. Nowadays, the deployment of Bluetooth nodes in industry generally relies on the experience knowledge of engineers and the cost of positioning beacon does not considered the global level. In this paper, we put forward a method to extract the number and location of BLE beacon automatically and ensure a high positioning accuracy of the indoor positioning system based the rules of indoor positioning, which use all kinds of space objects and structure characteristics of indoor map. The triangulation method was selected to study the global optimal placement of BLE beacon for localization based on indoor map. The impacts and requirements of BLE beacon placement were systematic analysed from the triangulation positioning method, indoor positioning environment and indoor user distribution characteristics. According to the characteristics of indoor environment structure and user distribution, we built an optimization model of BLE beacon placement method based on genetic algorithm which can generate the number and the location of BLE beacon. At last, the Bluetooth indoor positioning prototype system is developed to compare the experience method deployment scheme and the global optimization deployment scheme in the real indoor positioning environment.</p>

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.

scholarly journals An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7269
Author(s):  
Ling Ruan ◽  
Ling Zhang ◽  
Tong Zhou ◽  
Yi Long
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Indoor Positioning ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Local Clustering ◽  
Positioning Method ◽  
K Nearest Neighbor Algorithm ◽  
Space Range ◽  
Fingerprint Data

The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.

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 An Infrastructure-Free Indoor Localization Algorithm for Smartphones

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3317 ◽  
Author(s):  
Qu Wang ◽  
Haiyong Luo ◽  
Aidong Men ◽  
Fang Zhao ◽  
Yan Huang
Keyword(s):  
Magnetic Field ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Single Step ◽  
Tracking Algorithm ◽  
Shopping Mall ◽  
Observation Model ◽  
Ambient Light ◽  
Location Based Service ◽  
Localization Accuracy

Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g., Wi-Fi and Bluetooth-based methods) rely heavily on positioning infrastructure, which prevents their large-scale deployment and limits the range at which they are applicable. Here, we proposed an infrastructure-free indoor positioning and tracking approach, termed LiMag, which used ubiquitous magnetic field and ambient lights (e.g., fluorescent, incandescent, and light-emitting diodes (LEDs)) without containing modulated information. We conducted an in-depth study on both the advantages and the challenges in leveraging magnetic field and ambient light intensity for indoor localization. Based on the insights from this study, we established a hybrid observation model that took full advantage of both the magnetic field and ambient light signals. To address the low discernibility of the hybrid observation model, LiMag first generated a single-step fingerprint model by vectorizing consecutive hybrid observations within each step. In order to accurately track users, a lightweight single-step tracking algorithm based on the single-step fingerprints and the particle filter framework was designed. LiMag leveraged the walking information of users and several single-step fingerprints to generate long trajectory fingerprints that exhibited much higher location differentiation ability than the single-step fingerprint. To accelerate particle convergence and eliminate the accumulative error of single-step tracking algorithm, a long trajectory calibration scheme based on long trajectory fingerprints was also introduced. An undirected weighted graph model was constructed to decrease the computational overhead resulting from this long trajectory matching. In addition to typical indoor scenarios including offices, shopping malls and parking lots, we also conducted experiments in more challenging scenarios, including large open-plan areas as well as environments characterized by strong sunlight. Our proposed algorithm achieved a 75th percentile localization accuracy of 1.8 m and 2.2 m, respectively, in the office and shopping mall tested. In conclusion, our LiMag algorithm provided location-based service of infrastructure-free with significantly improved localization accuracy and coverage, as well as satisfactory robustness inside complex indoor environments.

scholarly journals Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints

2020 ◽  
Vol 9 (12) ◽  
pp. 714
Author(s):  
Yankun Wang ◽  
Renzhong Guo ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Indoor Positioning ◽  
Kriging Interpolation ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Position Information ◽  
Kriging Interpolation Method ◽  
Additional Equipment ◽  
Positioning Method ◽  
Fingerprint Database

Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.

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&rsquo;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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