Research and improvement on indoor localization based on RSSI fingerprint database and K-nearest neighbor points

2013 ◽  
Author(s):  
Zhang Guowei ◽  
Xu Zhan ◽  
Liu Dan
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
K Nearest Neighbor ◽  
Fingerprint Database

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 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.

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 Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System

2021 ◽  
pp. 79-96
Author(s):  
Dwi Suroso ◽  
Refa Rupaksi ◽  
Aditya Krisnawan ◽  
Nur Siddiq
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Random Forest Algorithm ◽  
K Nearest Neighbor ◽  
Received Signal Strength Indicator ◽  
Actual Measurement ◽  
Artificial Neural Network Ann ◽  
Device Free ◽  
Simple Parameter

The device-free indoor localization (DFIL) research is gaining attention due to the popularity of location-based service (LBS)-based advertisement. In DFIL, a user or an object does not need to bring any device to be localized. In this paper, we propose the Wi-Fi-based DFIL and the random forest algorithm for the fingerprint-based technique. The simple parameter commonly used in indoor localization is the Received Signal Strength Indicator (RSSI). We apply the fingerprint technique because of its reliability to handle the RSSI fluctuation and time-varying effect in a static indoor environment. We conducted an actual measurement campaign to observe the DFIL's implementation visibility. The DFIL system works by comparing the database fingerprint in an empty open office with the database in which a person is inside the measurement area without bringing any devices. Thus, we have the device-free RSSI database for fingerprint technique from both empty rooms and RSSI affected by a person inside the room. We validated the random forest algorithm results by comparing them with the k-nearest neighbor (kNN) and artificial neural network (ANN). The results show that our proposed system's accuracy is better than kNN and ANN with a mean error of 0.63 m than kNN with 0.80 m and ANN with 1.01 m. Meanwhile, the precision of the random forest is 0.63 m, whereas kNN and ANN are 0.67 m and 0.80 m, showing that the random forest performed better. We concluded that our simple DFIL system is visible to apply with acceptable accuracy performance.

scholarly journals Machine Learning Based Indoor Localization using Wi-Fi Fingerprinting

2019 ◽  
Vol 8 (3) ◽  
pp. 502-506
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
The Internet ◽  
Location Prediction ◽  
K Nearest Neighbor ◽  
Data Types ◽  
Specific Location ◽  
Web Access

The aim of indoor localization is to locate the objects inside a location wirelessly. This paper reports the models that predict the location along with floor and coordinates from the WAPs (Web Access Points) signal strengths of a user who connects to the internet at a specific location which had three locations. Starting with the cleaning of data, then assigning attributes into proper data types, making subset of dataset for each location, examining each column, and normalizing WAPs rows in order to build models. Different algorithms have been used to predict the location, floor, and coordinates of a logged in user. The models that have been used in this paper are k-Nearest Neighbor (k-NN) for location prediction, random forest for floor prediction and regression with k-NN for coordinate prediction.

scholarly journals Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1067 ◽  
Author(s):  
Chenbin Zhang ◽  
Ningning Qin ◽  
Yanbo Xue ◽  
Le Yang
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Hierarchical Classification ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Position Information ◽  
Area Of Interest

Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

scholarly journals Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5495
Author(s):  
Brahim El Boudani ◽  
Loizos Kanaris ◽  
Akis Kokkinis ◽  
Michalis Kyriacou ◽  
Christos Chrysoulas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Mobile Station ◽  
Access Point ◽  
Indoor Navigation ◽  
Raspberry Pi ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Smart Factories

In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION

2009 ◽  
Vol 09 (02) ◽  
pp. 171-199 ◽  
Author(s):  
SHANKAR BHAUSAHEB NIKAM ◽  
SUNEETA AGARWAL
Keyword(s):  
Nearest Neighbor ◽  
Texture Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Network Support ◽  
Wavelet Energy ◽  
Texture Characteristics ◽  
Energy Signature ◽  
Occurrence Matrix ◽  
Fingerprint Database

Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

scholarly journals A novel method of adaptive weighted K-nearest neighbor fingerprint indoor positioning considering user’s orientation

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878588 ◽  
Author(s):  
Jingxue Bi ◽  
Yunjia Wang ◽  
Xin Li ◽  
Hongji Cao ◽  
Hongxia Qi ◽  
...  
Keyword(s):  
Human Body ◽  
Nearest Neighbor ◽  
Reference Points ◽  
Average Error ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Factors Affecting ◽  
Positioning Method ◽  
Hybrid Distance ◽  
Fingerprint Database

There are many factors affecting Wi-Fi signal in indoor environment, among which the human body has an important impact. And, its characteristic is related to the user’s orientation. To eliminate positioning errors caused by user’s human body and improve positioning accuracy, this study puts forward an adaptive weighted K-nearest neighbor fingerprint positioning method considering the user’s orientation. First, the orientation fingerprint database model is proposed, which includes the position, orientation, and the sequence of mean received signal strength indicator at each reference point. Second, the fuzzy c-means algorithm is used to cluster orientation fingerprint database taking the hybrid distance of the signal domain and position domain as the clustering feature. Finally, the proposed adaptive algorithm is developed to select K-reference points by matching operation, to remove the reference points with larger signal-domain distances, minimum and maximum coordinate values, and calculate the weighted mean coordinates of the remaining reference points for positioning results. The experimental results show that the average error decreases by 0.7 m, and the root mean square error decreases to about 1.3 m by the proposed technique. And, we conclude that the proposed adaptive weighted K-nearest neighbor fingerprint positioning method can improve positioning accuracy.

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