scholarly journals A three-dimensional pattern recognition localization system based on a Bayesian graphical model

2020 ◽  
Vol 16 (9) ◽  
pp. 155014771988489 ◽  
Author(s):  
Abdulraqeb Alhammadi ◽  
Fazirulhisyam Hashim ◽  
Mohd. Fadlee A Rasid ◽  
Saddam Alraih
Keyword(s):  
Graphical Model ◽  
Indoor Localization ◽  
Three Dimensional ◽  
Signal Strength ◽  
Local Area ◽  
Received Signal Strength ◽  
Reference Points ◽  
Indoor Environments ◽  
Localization System ◽  
Proposed Model

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

scholarly journals Accurate indoor positioning system based on modify nearest point technique

2022 ◽  
Vol 12 (2) ◽  
pp. 1593
Author(s):  
Omar Ibrahim Mustafa ◽  
Hawraa Lateef Joey ◽  
Noor Abd AlSalam ◽  
Ibrahim Zeghaiton Chaloob
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Real Data ◽  
Reference Points ◽  
Positioning System ◽  
Indoor Environments ◽  
Nearest Point ◽  
The Impact ◽  
Indoor Positioning System

Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).

Improve the Robustness of Indoor Localization Using DRSS

2014 ◽  
Vol 556-562 ◽  
pp. 5922-5925
Author(s):  
Peng Xie
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Localization System ◽  
Heterogeneous Devices ◽  
Positioning Technique ◽  
Better Than

In this paper, we present a fingerprint positioning technique based on differential received signal strength (DRSS).By using DRSS fingerprints, the problem of degraded accuracy caused by heterogeneous devices in indoor WLAN localization. The robustness of DRSS fingerprints is proved, both theoretically and practically, to be better than that of RSS, thus the applicability as well as accuracy is improved in a WLAN indoor localization system with various devices.

scholarly journals A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771877968 ◽  
Author(s):  
Haibin Tong ◽  
Qingxu Deng ◽  
Tianyu Zhang ◽  
Yuanguo Bi
Keyword(s):  
Indoor Localization ◽  
Behavior Pattern ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Harsh Environments ◽  
Monitoring And Control ◽  
Distance Ratio ◽  
Received Signal Strength Indicator ◽  
Localization System ◽  
Acceptable Accuracy

Indoor localization systems using received signal strength indicator are very popular for their low power and low complexity, but some drawbacks limit their accuracy, especially in harsh environments, such as multipath and fluctuation. Most existing approaches solve the problem by “fingerprinting.” However, “fingerprinting” based algorithms are unsuitable for changeable environments like construction, since they all demand prior knowledge of the environment. This article studies a novel localization system to achieve an acceptable accuracy position using received signal strength indicator for harsh environments like construction. Based on analysis of the targets’ behavior pattern, we first use curve fitting to filter the distance derived from received signal strength indicator. And then, we propose a distance ratio location algorithm to estimate the targets’ positions. Furthermore, Kalman filter is considered to smooth the position results. This method has been applied in the “Monitoring and Control System for Underground Tunneling Based on Cyber Physical System” Project in Wuhan for tracking workers and vehicles. Practice results show that our system has an acceptable accuracy.

scholarly journals An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

2019 ◽  
Vol 11 (16) ◽  
pp. 1912 ◽  
Author(s):  
Tao Liu ◽  
Xing Zhang ◽  
Qingquan Li ◽  
Zhixiang Fang ◽  
Nadeem Tahir
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Reference Points ◽  
External Information ◽  
Calibration Error ◽  
K Nearest Neighbor ◽  
Initial Location ◽  
Radio Map

One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

scholarly journals End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi

2021 ◽  
Author(s):  
Mukhamet Nurpeiissov ◽  
Askat Kuzdeuov ◽  
Aslan Assylkhanov, ◽  
Yerbolat Khassanov ◽  
Hüseyin Atakan Varol
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors ◽  
Sampling Rate ◽  
Reference Points ◽  
Measurement Unit ◽  
Localization System ◽  
Practical Applications ◽  
External Data ◽  
End To End ◽  
Commercial Off The Shelf

This paper addresses sequential indoor localization using WiFi and Inertial Measurement Unit (IMU) modules commonly found in commercial off-the-shelf smartphones. Specifically, we developed an end-to-end neural network-based localization system integrating WiFi received signal strength indicator (RSSI) and IMU data without external data fusion models. The developed system leverages the advantages of WiFi and IMU modules to locate finer-level sequential positions of a user at 150 Hz sampling rate. Additionally, to demonstrate the efficacy of the proposed approach, we created the IMUWiFine dataset comprising IMU and WiFi RSSI readings sequentially collected at fine-level reference points. The dataset contains 120 trajectories covering an aggregate distance of over 14 kilometers. We conducted extensive experiments using deep learning models and achieved a mean error distance of 1.1 meters on an unseen evaluation set, which makes our approach suitable for many practical applications requiring meter-level accuracy. To enable experiment and result reproducibility, we made the developed localization system and IMUWiFine dataset publicly available in our GitHub repository.<br>

Robustness in Fingerprinting-Based Indoor Positioning Systems

2018 ◽  
pp. 88-141 ◽  
Author(s):  
Shih-Hau Fang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Signal Strength ◽  
Histogram Equalization ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Location Based Services ◽  
Indoor Environments ◽  
Positioning Systems

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

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