Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges.

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Indoor Floor Localization Based on Multi-Intelligent Sensors

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010006 ◽

2020 ◽

Vol 10 (1) ◽

pp. 6

Author(s):

Min Zhao ◽

Danyang Qin ◽

Ruolin Guo ◽

Xinxin Wang

Keyword(s):

Machine Learning ◽

Indoor Localization ◽

Collaborative Work ◽

Magnetic Data ◽

Data Mapping ◽

Localization Accuracy ◽

Database Construction ◽

Intelligent Sensors ◽

Majority Principle ◽

Fingerprint Database

With the continuous expansion of the market of indoor localization, the requirements of indoor localization technology are becoming higher and higher. Existing indoor floor localization (IFL) systems based on Wi-Fi signal and barometer data are susceptible to external environment changes, resulting in large errors. A method for indoor floor localization using multiple intelligent sensors (MIS-IFL) is proposed to decrease the localization errors, which consists of a fingerprint database construction phase and a floor localization phase. In the fingerprint database construction phase, data acquisition is performed using magnetometer sensor, accelerator sensor and gyro sensor in the smartphone. In the floor localization phase, an active pattern recognition is performed through the collaborative work of multiple intelligent sensors and machine learning classifiers. Then floor localization is performed using magnetic data mapping, Euclidean closest approximation and majority principle. Finally, the inter-floor detection link based on machine learning is added to improve the overall localization accuracy of MIS-IFL. The experimental results show that the performance of the proposed method is superior to the existing IFL.

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An Efficient Fingerprint Database Construction Approach Based on Matrix Completion for Indoor Localization

IEEE Access ◽

10.1109/access.2020.3009441 ◽

2020 ◽

Vol 8 ◽

pp. 130708-130718

Author(s):

Teng Tan ◽

Lingwen Zhang ◽

Qiumei Li

Keyword(s):

Indoor Localization ◽

Matrix Completion ◽

Database Construction ◽

Fingerprint Database

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A Two-Level WiFi Fingerprint-Based Indoor Localization Method for Dangerous Area Monitoring

Sensors ◽

10.3390/s19194243 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4243 ◽

Cited By ~ 1

Author(s):

Fei Li ◽

Min Liu ◽

Yue Zhang ◽

Weiming Shen

Keyword(s):

Indoor Localization ◽

Search Space ◽

High Accuracy ◽

Cluster Center ◽

Support Vector ◽

Localization Accuracy ◽

Localization Method ◽

Fingerprint Database ◽

Positioning Algorithm ◽

Area Monitoring

Localization technologies play an important role in disaster management and emergence response. In areas where the environment does not change much after an accident or in the case of dangerous areas monitoring, indoor fingerprint-based localization can be used. In such scenarios, a positioning system needs to have both a high accuracy and a rapid response. However, these two requirements are usually conflicting since a fingerprint-based indoor localization system with high accuracy usually has complex algorithms and needs to process a large amount of data, and therefore has a slow response. This problem becomes even worse when both the size of monitoring area and the number of reference nodes increase. To address this challenging problem, this paper proposes a two-level positioning algorithm in order to improve both the accuracy and the response time. In the off-line stage, a fingerprint database is divided into several sub databases by using an affinity propagation clustering (APC) algorithm based on Shepard similarity. The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy. Both experiment results and actual implementations proved that the proposed two-level localization method is more suitable than other methods in term of algorithm complexity, storage requirements and localization accuracy in dangerous area monitoring.

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Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Emerging Science Journal ◽

10.28991/esj-2021-sp1-012 ◽

2022 ◽

Vol 4 ◽

pp. 167-189

Author(s):

Dwi Joko Suroso ◽

Farid Yuli Martin Adiyatma ◽

Panarat Cherntanomwong ◽

Pitikhate Sooraksa

Keyword(s):

Indoor Localization ◽

Polynomial Regression ◽

Polynomial Interpolation ◽

Received Signal Strength Indicator ◽

Database Construction ◽

3D Environments ◽

Regression Techniques ◽

Fingerprint Database ◽

2D And 3D ◽

Classical Interpolation

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

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An Unsupervised Learning Technique to Optimize Radio Maps for Indoor Localization

Sensors ◽

10.3390/s19040752 ◽

2019 ◽

Vol 19 (4) ◽

pp. 752 ◽

Cited By ~ 5

Author(s):

Jens Trogh ◽

Wout Joseph ◽

Luc Martens ◽

David Plets

Keyword(s):

Unsupervised Learning ◽

Indoor Localization ◽

Inertial Sensor ◽

Ground Truth ◽

Training Data ◽

Model Based ◽

Site Survey ◽

Strength Based ◽

Fingerprint Database ◽

Radio Map

A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m2, resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data.

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An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽

10.3390/e23050574 ◽

2021 ◽

Vol 23 (5) ◽

pp. 574

Author(s):

Chendong Xu ◽

Weigang Wang ◽

Yunwei Zhang ◽

Jie Qin ◽

Shujuan Yu ◽

...

Keyword(s):

Channel State Information ◽

Indoor Environment ◽

Indoor Localization ◽

Location Based Services ◽

Channel State ◽

State Information ◽

Localization Accuracy ◽

Localization System ◽

Increasing Demand ◽

Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

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WiFi RSS fingerprint database construction for mobile robot indoor positioning system

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc.2016.7844461 ◽

2016 ◽

Cited By ~ 11

Author(s):

A.H. Ismail ◽

H. Kitagawa ◽

R. Tasaki ◽

K. Terashima

Keyword(s):

Mobile Robot ◽

Indoor Positioning ◽

Positioning System ◽

Database Construction ◽

Fingerprint Database ◽

Indoor Positioning System

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DAFI

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3494954 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-21

Author(s):

Hang Li ◽

Xi Chen ◽

Ju Wang ◽

Di Wu ◽

Xue Liu

Keyword(s):

Indoor Localization ◽

Domain Adaptation ◽

Target Domain ◽

Localization Accuracy ◽

Source Domain ◽

Fine Grained ◽

Time Signals ◽

Environmental Variations ◽

Device Free ◽

Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

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