Indoor positioning system based on improved Weighted K-nearest Neighbor

With rapid growth in the demand of location-based services (LBS) in indoor environments, localizations based on fingerprinting have attracted significant interest due to their convenience. Until now, most such methods were based on received signal strength indicator (RSSI), which is vulnerable to non-line-of-sight (NLOS). In order to realize high-precision indoor positioning, we propose a channel state information (CSI)-based Amp-Phi indoor-positioning system which exploits the amplitude and phase information of CSI at the same time to establish a fingerprinting database. Firstly, according to the characteristics of the raw CSI information collected at different positions under different environments, we build an NLOS mitigation model and a phase error mitigation model, respectively, to process the amplitude and phase of CSI. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After being processed with the models, the CSI features can be used to distinguish different positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build a fingerprinting database based on the features of amplitude and phase, realize to locate the target’s position with the K-Nearest Neighbor (KNN) matching algorithm. Experiments implemented in different situations show that Amp-Pi system is reliable and robust, whose position accuracy is higher than that of PhaseFi, Horus and machine learning (ML) systems under the same condition. It can be used in many scenarios, such as the localization of robots in our daily life, by doctors or patients in the hospital, for people localization in large supermarkets or museums and so on.

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CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v12i1.7632 ◽

2018 ◽

Vol 12 (1) ◽

pp. 61 ◽

Cited By ~ 7

Author(s):

Yohanes Erwin Dari ◽

Suyoto Suyoto Suyoto ◽

Pranowo Pranowo Pranowo

Keyword(s):

Nearest Neighbor ◽

Access Point ◽

Indoor Positioning ◽

Location Based Services ◽

Positioning System ◽

K Nearest Neighbor ◽

Indoor Location ◽

Pointing Device ◽

Accurate Position ◽

Indoor Positioning System

The existence of mobile devices as a location pointing device using Global Positioning System (GPS) is a very common thing nowadays. The use of GPS as a tool to determine the location of course has a shortage when used indoors. Therefore, the used of indoor location-based services in a room that leverages the use of Access Point (AP) is very important. By using the information of the Received Signal Strength (RSS) obtained from AP, then the location of the device can be determined without the need to use GPS. This technique is called the location fingerprint technique using the characteristics of received RSS’s fingerprint, then use it to determine the position. To get a more accurate position then authors used the K-Nearest Neighbor (KNN) method. KNN will use some of the data that obtained from some AP to assist in positioning the device. This solution of course would be able to determine the position of the devices in a storied building.

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A VLC-Based 3-D Indoor Positioning System Using Fingerprinting and K-Nearest Neighbor

2017 IEEE 85th Vehicular Technology Conference (VTC Spring) ◽

10.1109/vtcspring.2017.8108345 ◽

2017 ◽

Cited By ~ 1

Author(s):

Ming Xu ◽

Weiwei Xia ◽

Ziyan Jia ◽

Yaping Zhu ◽

Lianfeng Shen

Keyword(s):

Nearest Neighbor ◽

Indoor Positioning ◽

Positioning System ◽

K Nearest Neighbor ◽

Indoor Positioning System

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Convex Optimization via Symmetrical Hölder Divergence for a WLAN Indoor Positioning System

Entropy ◽

10.3390/e20090639 ◽

2018 ◽

Vol 20 (9) ◽

pp. 639 ◽

Cited By ~ 5

Author(s):

Osamah Abdullah

Keyword(s):

Nearest Neighbor ◽

Indoor Localization ◽

Healthcare Providers ◽

Indoor Positioning ◽

Location Based Services ◽

Positioning System ◽

K Nearest Neighbor ◽

Multivariate Distributions ◽

Major Drawback ◽

Indoor Positioning System

Modern indoor positioning system services are important technologies that play vital roles in modern life, providing many services such as recruiting emergency healthcare providers and for security purposes. Several large companies, such as Microsoft, Apple, Nokia, and Google, have researched location-based services. Wireless indoor localization is key for pervasive computing applications and network optimization. Different approaches have been developed for this technique using WiFi signals. WiFi fingerprinting-based indoor localization has been widely used due to its simplicity, and algorithms that fingerprint WiFi signals at separate locations can achieve accuracy within a few meters. However, a major drawback of WiFi fingerprinting is the variance in received signal strength (RSS), as it fluctuates with time and changing environment. As the signal changes, so does the fingerprint database, which can change the distribution of the RSS (multimodal distribution). Thus, in this paper, we propose that symmetrical Hölder divergence, which is a statistical model of entropy that encapsulates both the skew Bhattacharyya divergence and Cauchy–Schwarz divergence that are closed-form formulas that can be used to measure the statistical dissimilarities between the same exponential family for the signals that have multivariate distributions. The Hölder divergence is asymmetric, so we used both left-sided and right-sided data so the centroid can be symmetrized to obtain the minimizer of the proposed algorithm. The experimental results showed that the symmetrized Hölder divergence consistently outperformed the traditional k nearest neighbor and probability neural network. In addition, with the proposed algorithm, the position error accuracy was about 1 m in buildings.

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Effect of Sample Sizes in Fingerprinting Database for Wi-Fi System

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2021.6.4394 ◽

2021 ◽

Vol 16 (6) ◽

Author(s):

Ahmad Hakimi Bin Ahmad Sa'ahiry ◽

Abdul Halim Ismail ◽

Latifah Munirah Kamaruddin ◽

Mohd Sani Mohamad Hashim ◽

Muhamad Safwan Muhamad Azmi ◽

...

Keyword(s):

Sample Size ◽

Indoor Positioning ◽

Location Estimation ◽

Global Navigation Satellite Systems ◽

Positioning System ◽

Shadow Fading ◽

Sample Sizes ◽

K Nearest Neighbor ◽

Fingerprinting Method ◽

Indoor Positioning System

Indoor positioning system has been an essential work to substitute the Global Positioning System (GPS). GPS utilizing Global Navigation Satellite Systems (GNSS) cannot provide an accurate positioning in the indoor due to the multipath effect and shadow fading. Fingerprinting method with Wi-Fi technology is a promising system to solve this issue. However, there are several problems with the fingerprinting method. The fingerprinting database collected has different sample sizes where the previous researcher does not indicate any standard for the sample size to be used. In this paper, the effect of the sample sizes in fingerprinting database for Wi-Fi technology has been discussed deeply. The statistical analyzation for different sample sizes has been analyzed. Furthermore, two methods which are K- Nearest Neighbor (KNN) and Deep Neural Network (DNN) are being used to examine the effect of the sample sizes in term of accuracy and distance error. The discussion in this paper will contribute to the better sample size selection depending on the method taken by the user. The result shows that sample sizes are an important metrics in developing the indoor positioning system as it effects the result of the location estimation.

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