Probability-Based Indoor Positioning Algorithm Using iBeacons

High-precision indoor positioning is important for modern society. This paper proposes a way to achieve high positioning accuracy and obtain a trajectory close to the actual path in a common application scenario by smartphone without the use of a complicated algorithm. In the actual positioning process, a stable signal source can reduce the signal interference caused by environments. Bluetooth low energy has its own advantages in indoor positioning because it can be seen as a more stable signal source. In this study, we used smartphones to record the changing Bluetooth signals and used a basic nearest neighbor, weight centroid, and probability-based method, which we called an advanced weighted centroid method, to obtain position coordinates and the motion trajectory during the experiment. We used a weight centroid method based on least squares to solve the overdetermined problem. This can also be used to calculate the initial position of the advanced weight centroid. The advanced weighted centroid method introduced a Gaussian distribution to model the distribution of the signal. Translating a deterministic problem into a fuzzy probability problem aligns more with positioning facts and can achieve better results. Experimental results showed that the root-mean-square error (RMSE) of the dynamic positioning result obtained through the probabilistic method was within 1 m and had a more consistent trajectory. Moreover, the impact of the number of iBeacons on the positioning accuracy has been discussed, and a reference for iBeacon placement has been provided. In addition, an experiment was also conducted on the effect of signal transmission frequency on accuracy.

Download Full-text

Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Sensors ◽

10.3390/s19194351 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4351 ◽

Cited By ~ 9

Author(s):

Ashraf ◽

Hur ◽

Park

Keyword(s):

Human Mobility ◽

Indoor Positioning ◽

Location Based Services ◽

Indoor Environments ◽

Coverage Area ◽

Positioning Accuracy ◽

Positioning Systems ◽

Access Points ◽

Intersection Area ◽

The Impact

The applications of location-based services require precise location information of a user both indoors and outdoors. Global positioning system’s reduced accuracy for indoor environments necessitated the initiation of Indoor Positioning Systems (IPSs). However, the development of an IPS which can determine the user’s position with heterogeneous smartphones in the same fashion is a challenging problem. The performance of Wi-Fi fingerprinting-based IPSs is degraded by many factors including shadowing, absorption, and interference caused by obstacles, human mobility, and body loss. Moreover, the use of various smartphones and different orientations of the very same smartphone can limit its positioning accuracy as well. As Wi-Fi fingerprinting is based on Received Signal Strength (RSS) vector, it is prone to dynamic intrinsic limitations of radio propagation, including changes over time, and far away locations having similar RSS vector. This article presents a Wi-Fi fingerprinting approach that exploits Wi-Fi Access Points (APs) coverage area and does not utilize the RSS vector. Using the concepts of APs coverage area uniqueness and coverage area overlap, the proposed approach calculates the user’s current position with the help of APs’ intersection area. The experimental results demonstrate that the device dependency can be mitigated by making the fingerprinting database with the proposed approach. The experiments performed at a public place proves that positioning accuracy can also be increased because the proposed approach performs well in dynamic environments with human mobility. The impact of human body loss is studied as well.

Download Full-text

An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window

Sensors ◽

10.3390/s20247269 ◽

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.

Download Full-text

An RSSD-Based Fingerprint Positioning Method for Detection of an Unknown Radio Transmitter Using WLS and Factor Graph

Mathematical Problems in Engineering ◽

10.1155/2018/8052415 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11

Author(s):

Liyang Zhang ◽

Taihang Du ◽

Chundong Jiang

Keyword(s):

Nearest Neighbor ◽

Access Point ◽

Reference Points ◽

Least Square ◽

Factor Graph ◽

K Nearest Neighbor ◽

Positioning Accuracy ◽

Radio Transmitter ◽

Error Weight ◽

The Impact

Realizing accurate detection of an unknown radio transmitter (URT) has become a challenge problem due to its unknown parameter information. A method based on received signal strength difference (RSSD) fingerprint positioning technique and using factor graph (FG) has been successfully developed to achieve the localization of an URT. However, the RSSD-based FG model is not accurate enough to express the relationship between the RSSD and the corresponding location coordinates since the RSSD variances of reference points are different in practice. This paper proposes an enhanced RSSD-based FG algorithm using weighted least square (WLS) to effectively reduce the impact of RSSD measurement variance difference on positioning accuracy. By the use of stochastic RSSD errors between the measured value and the estimated value of the selected reference points, we utilize the error weight matrix to establish a new WLSFG model. Then, the positioning process of proposed RSSD-WLSFG algorithm is derived with the sum-product principle. In addition, the paper also explores the effects of different access point (AP) numbers and grid distances on positioning accuracy. The simulation experiment results show that the proposed algorithm can obtain the best positioning performance compared with the conventional RSSD-based K nearest neighbor (RSSD-KNN) and RSSD-FG algorithms in the case of different AP numbers and grid distances.

Download Full-text

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.

Download Full-text

An RSS Transform—Based WKNN for Indoor Positioning

Sensors ◽

10.3390/s21175685 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5685

Author(s):

Rong Zhou ◽

Yexi Yang ◽

Puchun Chen

Keyword(s):

Real Time ◽

Nearest Neighbor ◽

Environmental Changes ◽

Indoor Positioning ◽

Test Point ◽

Gaussian Kernel ◽

Support Vector ◽

K Nearest Neighbor ◽

Positioning Accuracy ◽

Time Performance

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

Download Full-text

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.

Download Full-text

Impact of Beacon-Dependent Parameters on Bluetooth Low Energy Indoor Positioning Accuracy

Proceedings ◽

10.3390/proceedings2191223 ◽

2018 ◽

Vol 2 (19) ◽

pp. 1223 ◽

Cited By ~ 1

Author(s):

Gabriel de Blasio ◽

Alexis Quesada-Arencibia ◽

José Carlos Rodríguez-Rodríguez ◽

Carmelo R. García ◽

Roberto Moreno-Díaz Jr.

Keyword(s):

Data Collection ◽

Automatic System ◽

Indoor Positioning ◽

Low Energy ◽

Transmission Power ◽

Energy Technology ◽

Positioning Accuracy ◽

Positioning Systems ◽

Ubiquitous Systems ◽

The Impact

Blue Low Energy technology is playing an important role nowadays in ubiquitous systems, being the beacons a key element. The configuration of parameters related to the beacons, such as their transmission power or their advertising interval should be studied in order to build fingerprinting indoor positioning systems based on this technology as accurate as possible. In this work, we study the impact and the interplay of those parameters in static indoor positioning as well as the orientation effect in the calibration phase. To reduce the time of data collection, a semi-automatic system is introduced.

Download Full-text

Research on improved indoor positioning algorithm based on WiFi–pedestrian dead reckoning

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719851932 ◽

2019 ◽

Vol 15 (5) ◽

pp. 155014771985193

Author(s):

Guanghua Zhang ◽

Xue Sun ◽

Jingqiu Ren ◽

Weidang Lu

Keyword(s):

Nearest Neighbor ◽

Dead Reckoning ◽

Signal Strength ◽

K Nearest Neighbor ◽

Received Signal Strength Indicator ◽

Positioning Accuracy ◽

Positioning Algorithm ◽

And Control ◽

The Impact ◽

Pedestrian Dead Reckoning

In order to improve the positioning accuracy and reduce the impact of indoor complex environment on WiFi positioning results, an improved fusion positioning algorithm based on WiFi–pedestrian dead reckoning is proposed. The algorithm uses extended Kalman filter as the fusion positioning filter of WiFi–pedestrian dead reckoning. Aiming at the problem of WiFi signal strength fluctuation, Bayesian estimation matching algorithm based on K-nearest neighbor is proposed to reduce the impact of the dramatic change of received signal strength indicator value on the positioning result effectively. For the cumulative error problem in pedestrian dead reckoning positioning algorithm, a post-correction module is used to reduce the error. The experimental results show that the algorithm can improve the shortcomings of these two algorithms and control the positioning accuracy within 1.68 m.

Download Full-text

The Process of Globalization and Its Impact on Media

European Journal of Multidisciplinary Studies ◽

10.26417/ejms.v1i4.p150-153 ◽

2016 ◽

Vol 1 (4) ◽

pp. 150

Author(s):

Veton Zejnullahi

Keyword(s):

New Technology ◽

World Order ◽

Modern Society ◽

Media Access ◽

Four Corners ◽

The World ◽

The Media ◽

The Republic ◽

Human Eyes ◽

The Impact

The process of globalization, which many times is considered as new world order is affecting all spheres of modern society but also the media. In this paper specifically we will see the impact of globalization because we see changing the media access to global problems in general being listed on these processes. We will see that the greatest difficulties will have small media as such because the process is moving in the direction of creating mega media which thanks to new technology are reaching to deliver news and information at the time of their occurrence through choked the small media. So it is fair to conclude that the rapid economic development and especially the technology have made the world seem "too small" to the human eyes, because for real-time we will communicate with the world with the only one Internet connection, and also all the information are take for the development of events in the four corners of the world and direct from the places when the events happen. Even Albanian space has not left out of this process because the media in the Republic of Albania and the Republic of Kosovo are adapted to the new conditions under the influence of the globalization process. This fact is proven powerful through creating new television packages, written the websites and newspapers in their possession.

Download Full-text

Feasibility of Using Floor Vibration to Detect Human Falls

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010200 ◽

2020 ◽

Vol 18 (1) ◽

pp. 200

Author(s):

Yu Shao ◽

Xinyue Wang ◽

Wenjie Song ◽

Sobia Ilyas ◽

Haibo Guo ◽

...

Keyword(s):

Machine Learning ◽

Human Body ◽

Nearest Neighbor ◽

Body Movement ◽

Modern Society ◽

Recognition System ◽

Machine Learning Algorithms ◽

K Nearest Neighbor ◽

Study Results ◽

Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

Download Full-text