scholarly journals Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1015
Author(s):  
Yuqing Yin ◽  
Xu Yang ◽  
Peihao Li ◽  
Kaiwen Zhang ◽  
Pengpeng Chen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Bayesian Model Averaging ◽  
Indoor Localization ◽  
Domain Adaptation ◽  
Model Averaging ◽  
Indoor Environments ◽  
Test Bed ◽  
Localization Accuracy ◽  
Fine Grained ◽  
Multiple Domains

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

DAFI

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.

Improving UWB Indoor Localization Accuracy Using Sparse Fingerprinting and Transfer Learning

2021 ◽  
pp. 291-302
Author(s):  
Krzysztof Adamkiewicz ◽  
Piotr Koch ◽  
Barbara Morawska ◽  
Piotr Lipiński ◽  
Krzysztof Lichy ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Indoor Localization ◽  
Localization Accuracy

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.

Improved SfM-Based Indoor Localization with Occlusion Removal

2018 ◽  
Vol 10 (3) ◽  
pp. 24-40
Author(s):  
Yushi Li ◽  
George Baciu ◽  
Yu Han ◽  
Chenhui Li
Keyword(s):  
Indoor Localization ◽  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Data Sets ◽  
Indoor Environments ◽  
Decomposition Approach ◽  
Localization Accuracy ◽  
Occlusion Removal ◽  
Accuracy Increase

This article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.

scholarly journals The Indoor Localization of a Mobile Platform Based on Monocular Vision and Coding Images

2020 ◽  
Vol 9 (2) ◽  
pp. 122
Author(s):  
Fei Liu ◽  
Jixian Zhang ◽  
Jian Wang ◽  
Binghao Li
Keyword(s):  
Good Accuracy ◽  
Indoor Localization ◽  
Unit Weight ◽  
Indoor Environments ◽  
Mobile Platforms ◽  
Localization Accuracy ◽  
Indoor Space ◽  
Positioning Method ◽  
Spatial Coordinates ◽  
Development And Utilization

With the extensive development and utilization of urban underground space, coal mines, and other indoor areas, the indoor positioning technology of these areas has become a hot research topic. This paper proposes a robust localization method for indoor mobile platforms. Firstly, a series of coding graphics were designed for localizing the platform, and the spatial coordinates of these coding graphics were calculated by using a new method proposed in this paper. Secondly, two spatial resection models were constructed based on unit weight and Tukey weight to localize the platform in indoor environments. Lastly, the experimental results show that both models can calculate the position of the platform with good accuracy. The space resection model based on Tukey weight correctly identified the residuals of the observations for calculating the weights to obtain robust positioning results and has a high positioning accuracy. The navigation and positioning method proposed in this study has a high localization accuracy and can be potentially used in localizing practical indoor space mobile platforms.

scholarly journals THE AUTOMATIC GENERATION OF AN ADAPTIVE NAVIGATION MODEL FOR INDOOR MAP MATCHING

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 895-902
Author(s):  
P. Wang ◽  
J. Shang ◽  
Z. Zhou ◽  
Y. Wu ◽  
W. Sun
Keyword(s):  
Spatial Model ◽  
Indoor Localization ◽  
Automatic Generation ◽  
Model Representation ◽  
Map Matching ◽  
Localization Accuracy ◽  
Fine Grained ◽  
Movement Characteristics

<p><strong>Abstract.</strong> Indoor map matching has been an important technique to improve the indoor localization accuracy because it takes the advantage of available indoor building data and effectively decreases the localization cost. One of the spatial model involved in the indoor map matching, adaptive navigation model, could balance the accuracy and complexity of the model representation required by map matching by combining the medial axes and fine-grained grids according to the movement characteristics of pedestrians in open and narrow areas. In order to reduce the manual effort of producting a large number of models and update them, we propose an algorithm to automatically generate this model. Futermore, we use this algorithm to generate this model for three lab architectures, and the evaluation of the results of the ANM generated by the algorithm proves that the algorithm meets the requirements.</p>

scholarly journals Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3933
Author(s):  
Mohammed El-Absi ◽  
Feng Zheng ◽  
Ashraf Abuelhaija ◽  
Ali Al-haj Abbas ◽  
Klaus Solbach ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Large Scale ◽  
Indoor Localization ◽  
Channel Modeling ◽  
Low Complexity ◽  
Small Scale ◽  
Indoor Environments ◽  
Wave Band ◽  
Localization Algorithm ◽  
Localization Accuracy

Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

scholarly journals Enhance RSS-Based Indoor Localization Accuracy by Leveraging Environmental Physical Features

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Peng Xiang ◽  
Peng Ji ◽  
Dian Zhang
Keyword(s):  
Indoor Localization ◽  
Radio Signal ◽  
Signal Strength ◽  
Indoor Environments ◽  
Wireless Devices ◽  
Positioning Accuracy ◽  
Localization Accuracy ◽  
Localization Method ◽  
Physical Features ◽  
Additional Hardware

Indoor localization technologies based on Radio Signal Strength (RSS) attract many researchers’ attentions, since RSS can be easily obtained by wireless devices without additional hardware. However, such technologies are apt to be affected by indoor environments and multipath phenomenon. Thus, the accuracy is very difficult to improve. In this paper, we put forward a method, which is able to leverage various other resources in localization. Besides the traditional RSS information, the environmental physical features, e.g., the light, temperature, and humidity information, are all utilized for localization. After building a comprehensive fingerprint map for the above information, we propose an algorithm to localize the target based on Naïve Bayesian. Experimental results show that the successful positioning accuracy can dramatically outperform traditional pure RSS-based indoor localization method by about 39%. Our method has the potential to improve all the radio frequency (RF) based localization approaches.

scholarly journals Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4783
Author(s):  
Gao ◽  
Zhang ◽  
Xiao ◽  
Li
Keyword(s):  
Channel State Information ◽  
Indoor Localization ◽  
Probabilistic Approach ◽  
Indoor Environments ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Sensing Applications ◽  
Leibler Divergence ◽  
Device Free

Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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