scholarly journals Indoor Localization Based on Fingerprint Clustering

2020 ◽  
Vol 5 (2) ◽  
pp. 40
Author(s):  
Shi Chen
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Rapid Development ◽  
Computational Cost ◽  
Gaussian Model ◽  
Location Based Services ◽  
Indoor Environments ◽  
Localization Error ◽  
Localization Algorithm ◽  
K Nearest Neighbor

With the rapid development of the huge promotion of the Internet and artificial intelligence, the demand for location-based services in indoor environments has grown rapidly. At present, for the localization of the indoor environment, researchers from all walks of life have proposed many indoor localization solutions based on different technologies. Fingerprint localization technology, as a commonly used indoor localization technology, has led to continuous research and improvement due to its low accuracy and complex calculations. An indoor localization system based on fingerprint clustering is proposed by this paper. The system includes offline phase and online phase. We collect the RSS signal in the offline phase. We preprocess it with the Gaussian model to build a fingerprint database, and then we use the K-Means++ algorithm to cluster the fingerprints and group the fingerprints with similar signal strengths into a clustering subset. In the online phase, we classify the measured received signal strength (RSS), and then use the weighted K-Nearest neighbor (WKNN) algorithm to calculate the localization error. The experimental results show that we can reduce the localization error and effectively reduce the computational cost of the localization algorithm in the online phase, and effectively improve the efficiency of real-time localization in the online phase.

scholarly journals A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 875 ◽  
Author(s):  
Xiaochao Dang ◽  
Xiong Si ◽  
Zhanjun Hao ◽  
Yaning Huang
Keyword(s):  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Signal Interference ◽  
Indoor Environments ◽  
Phase Information ◽  
Localization Accuracy ◽  
Localization Method ◽  
Indoor Location ◽  
Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

scholarly journals Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

2019 ◽  
Vol 9 (11) ◽  
pp. 2337 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Scene Recognition ◽  
Localization Error ◽  
K Nearest Neighbor ◽  
Current Location ◽  
Smartphone Sensors ◽  
The Impact ◽  
Localization Systems

Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians’ current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian’s current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.

Amp-Phi: A CSI-Based Indoor Positioning System

2018 ◽  
Vol 32 (09) ◽  
pp. 1858005 ◽  
Author(s):  
Tao-Yun Zhou ◽  
Bao-Wang Lian ◽  
Yi Zhang ◽  
Sen Liu
Keyword(s):  
Nearest Neighbor ◽  
Phase Error ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Statistical Characteristics ◽  
Positioning System ◽  
Indoor Environments ◽  
K Nearest Neighbor ◽  
Error Mitigation ◽  
Indoor Positioning System

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.

WiCLoc: A Novel CSI-based Fingerprint Localization System

2019 ◽  
Vol 34 (04) ◽  
pp. 2058003
Author(s):  
Lei Zhang ◽  
Yanjun Hu ◽  
Yafeng Liu ◽  
Jiaxiang Li ◽  
Enjie Ding
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Smart Devices ◽  
Fingerprint Matching ◽  
Wifi Networks ◽  
Localization System ◽  
Exhibition Hall ◽  
Localization Systems

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted [Formula: see text]-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85[Formula: see text]m in the exhibition hall and 1.28[Formula: see text]m in the laboratory, outperforming the other systems.

scholarly journals Indoor Localization Method Based on Regional Division with IFCM

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 559 ◽  
Author(s):  
Junhuai Li ◽  
Xixi Gao ◽  
Zhiyong Hu ◽  
Huaijun Wang ◽  
Ting Cao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Pearson Correlation ◽  
Computation Time ◽  
Access Point ◽  
Target Point ◽  
Localization Algorithm ◽  
K Nearest Neighbor ◽  
Localization Method ◽  
Regional Division

With the development of wireless technology, indoor localization has gained wide attention. The fingerprint localization method is proposed in this paper, which is divided into two phases: offline training and online positioning. In offline training phase, the Improved Fuzzy C-means (IFCM) algorithm is proposed for regional division. The Between-Within Proportion (BWP) index is selected to divide fingerprint database, which can ensure the result of regional division consistent with the building plane structure. Moreover, the Agglomerative Nesting (AGNES) algorithm is applied to accomplish Access Point (AP) optimization. In the online positioning phase, sub-region selection is performed by nearest neighbor algorithm, then the Weighted K-nearest Neighbor (WKNN) algorithm based on Pearson Correlation Coefficient (PCC) is utilized to locate the target point. After the evaluation on the effect of regional division and AP optimization of location precision and time, the experiments show that the average positioning error is 2.53 m and the average computation time of the localization algorithm based on PCC reduced by 94.13%.

scholarly journals BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4859 ◽  
Author(s):  
Mingfeng Li ◽  
Lichen Zhao ◽  
Ding Tan ◽  
Xiaozhe Tong
Keyword(s):  
Template Matching ◽  
Indoor Environment ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Strength ◽  
Reference Points ◽  
Localization Algorithm ◽  
K Nearest Neighbor ◽  
Coverage Area ◽  
Fingerprint Database

Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference points in the fingerprint database, the methods for the eight-neighborhood template matching and generation were studied. In this study, the indoor environment was divided into four quadrants for each access point and the expected values of the received signal strength indication (RSSI) difference between the center points and their eight-neighborhoods in different quadrants were chosen as the generation parameters. Then different templates were generated for different access points, and the unknown point was located by the Euclidean distance for the correlation of RSSI between each template and its coverage area in the fingerprint database. With the spatial correlation of fingerprint data taken into account, the influence of abnormal fingerprint on locating accuracy is reduced. The experimental results show that the locating error is 1.0 m, which is about 0.2 m less than both K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN) algorithms.

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

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 639 ◽  
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.

scholarly journals SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1323 ◽  
Author(s):  
Donald L. Hall ◽  
Ram M. Narayanan ◽  
David M. Jenkins
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
High Performance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Multipath Channel ◽  
K Nearest Neighbor ◽  
Positioning Systems ◽  
Channel Characteristics

Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.

scholarly journals An Algorithm for Accurate and Robust Indoor Localization Based on Nonlinear Programming

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 65 ◽  
Author(s):  
Stefania Monica ◽  
Federico Bergenti
Keyword(s):  
Nonlinear Programming ◽  
Mobile Devices ◽  
Optimization Problem ◽  
Indoor Localization ◽  
Wide Band ◽  
Location Based Services ◽  
Experimental Results ◽  
Indoor Environments ◽  
Anchor Nodes ◽  
The Impact

The study of techniques to estimate the position of mobile devices with a high level of accuracy and robustness is essential to provide advanced location based services in indoor environments. An algorithm to enable mobile devices to estimate their positions in known indoor environments is proposed in this paper under the assumption that fixed anchor nodes are available at known locations. The proposed algorithm is specifically designed to be executed on the mobile device whose position is under investigation, and it allows the device to estimate its position within the environment by actively measuring distance estimates from the anchor nodes. In order to reduce the impact of the errors caused by the arrangement of the anchor nodes in the environment, the proposed algorithm first transforms the localization problem into an optimization problem, and then, it solves the derived optimization problem using techniques inspired by nonlinear programming. Experimental results obtained using ultra-wide band signaling are presented to assess the performance of the algorithm and to compare it with reference alternatives. The presented experimental results confirm that the proposed algorithm provides an increased level of accuracy and robustness with respect to two reference alternatives, regardless of the position of the anchor nodes.

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