scholarly journals Using Acoustic Signal and Image to Achieve Accurate Indoor Localization

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2566 ◽  
Author(s):  
Rui Xi ◽  
Daibo Liu ◽  
Mengshu Hou ◽  
Yujun Li ◽  
Jun Li
Keyword(s):  
Pervasive Computing ◽  
Indoor Localization ◽  
Practical Application ◽  
Location Based Service ◽  
Localization Accuracy ◽  
Indoor Location ◽  
Location Service ◽  
Localization Scheme ◽  
True Location ◽  
Acoustic Sources

Location information plays a key role in pervasive computing and application, especially indoor location-based service, even though a mass of systems have been proposed, an accurate and practical indoor localization system remains unsettled. To tackle this issue, in this paper, we present a new localization scheme, SITE, combining acoustic Signals and Images to achieve accurate and robust indoor locaTion sErvice. Relying on a pre-deployed platform of acoustic sources with different frequencies, using proactively generated Doppler effect signals, SITE could track relative directions between the phone and the sources. Given m (m≥5) relative directions, SITE can use the angle differences to compute a set of locations corresponding to different subsets of sources. Then, based on a key observation—while the simultaneously estimated locations using different sets of acoustic anchors are within a small circle, the results converge to a point near the true location—SITE proposes a decision scheme that confirms whether these locations satisfy the demand of localization accuracy and can be used to search the user’s location. If not, SITE utilizes VSFM(Visual Structure from Motion) technique to achieve a set of relative locations using some images captured by the phone’s camera. By exploiting the synergy between the set of relative locations and the set of initial locations computed by relative directions, an optimal transformation relationship is obtained and applied to refine the initial calculated results. The refined result will be regarded as the user’s location. In the evaluation, we implemented a prototype and deployed a real platform of acoustic sources in different scenarios. Experimental results show that SITE has excellent performance of localization accuracy, robustness and feasibility in practical application.

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.

Accurate Dead Reckoning Localization System Using RFID Phase

2014 ◽  
Vol 513-517 ◽  
pp. 3296-3299 ◽  
Author(s):  
Bo Li ◽  
Dong Wang
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Location Based Service ◽  
Rfid Tag ◽  
Whole Process ◽  
Localization Scheme ◽  
Frequency Identification ◽  
Series Of Experiments

Nowadays, the demands of Location-based Service are growing fast. It contains huge business opportunities. This paper presents an efficient indoor localization scheme using Radio-Frequency Identification technology. The major idea of our method is Dead Reckoning, a method of navigation that using the best estimates of speed and direction to calculate users' motion trace. We implemented Dead Reckoning in indoor environment by taking advantage of features of RFID. We collected RFID tag phase value to calculate the velocity of users and recalibrate users' position by using known fixed RFID reader. We designed a series of experiments to verify the feasibility of our velocity calculation method, then we simulated the whole process of our system. The results show that our system can track user's motion effectively in indoor environment. We believe this is an encouraging result, holding promise for real-world deployment.

scholarly journals A Method to Incorporate Floor Plan Constraints into Indoor Location Tracking: A Voronoi Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
John D. Hobby ◽  
Marzieh Dashti
Keyword(s):  
Indoor Localization ◽  
Research Effort ◽  
Location Tracking ◽  
Floor Plan ◽  
Location Based Service ◽  
Indoor Location ◽  
Filtering Algorithm ◽  
Localization Algorithms ◽  
Specific Localization ◽  
Floor Plans

Indoor localization has attracted a lot of research effort in recent years due to the explosion of indoor location-based service (LBS) applications. Incorporating map constraints into localization algorithms reduces the uncertainty of walking trajectories and enhances location accuracy. Suitable maps for computer-aided localization algorithms are not readily available, and hence most researchers working on localization solutions manually create maps for their specific localization scenarios. This paper presents a method of generating indoor maps suitable for localization algorithms from CAD floor plans. Our solution is scalable for mass-market LBS deployment. We also propose an adapted map-filtering algorithm that utilizes map information extracted from CAD floor plans. We evaluate the performance of our solution via real-world Wi-Fi RF measurements.

scholarly journals An Infrastructure-Free Indoor Localization Algorithm for Smartphones

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3317 ◽  
Author(s):  
Qu Wang ◽  
Haiyong Luo ◽  
Aidong Men ◽  
Fang Zhao ◽  
Yan Huang
Keyword(s):  
Magnetic Field ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Single Step ◽  
Tracking Algorithm ◽  
Shopping Mall ◽  
Observation Model ◽  
Ambient Light ◽  
Location Based Service ◽  
Localization Accuracy

Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g., Wi-Fi and Bluetooth-based methods) rely heavily on positioning infrastructure, which prevents their large-scale deployment and limits the range at which they are applicable. Here, we proposed an infrastructure-free indoor positioning and tracking approach, termed LiMag, which used ubiquitous magnetic field and ambient lights (e.g., fluorescent, incandescent, and light-emitting diodes (LEDs)) without containing modulated information. We conducted an in-depth study on both the advantages and the challenges in leveraging magnetic field and ambient light intensity for indoor localization. Based on the insights from this study, we established a hybrid observation model that took full advantage of both the magnetic field and ambient light signals. To address the low discernibility of the hybrid observation model, LiMag first generated a single-step fingerprint model by vectorizing consecutive hybrid observations within each step. In order to accurately track users, a lightweight single-step tracking algorithm based on the single-step fingerprints and the particle filter framework was designed. LiMag leveraged the walking information of users and several single-step fingerprints to generate long trajectory fingerprints that exhibited much higher location differentiation ability than the single-step fingerprint. To accelerate particle convergence and eliminate the accumulative error of single-step tracking algorithm, a long trajectory calibration scheme based on long trajectory fingerprints was also introduced. An undirected weighted graph model was constructed to decrease the computational overhead resulting from this long trajectory matching. In addition to typical indoor scenarios including offices, shopping malls and parking lots, we also conducted experiments in more challenging scenarios, including large open-plan areas as well as environments characterized by strong sunlight. Our proposed algorithm achieved a 75th percentile localization accuracy of 1.8 m and 2.2 m, respectively, in the office and shopping mall tested. In conclusion, our LiMag algorithm provided location-based service of infrastructure-free with significantly improved localization accuracy and coverage, as well as satisfactory robustness inside complex indoor environments.

scholarly journals INDOOR SPACE LOCATION MODEL BASED ON LOCATION SERVICE

2017 ◽  
Vol XLII-4/W7 ◽  
pp. 49-53
Author(s):  
Y. Zhou ◽  
G. Zeng ◽  
Y. Huang ◽  
X. Yang
Keyword(s):  
Spatial Information ◽  
Indoor Navigation ◽  
Location Model ◽  
Location Information ◽  
Location Based Service ◽  
Location Services ◽  
Indoor Location ◽  
Indoor Space ◽  
Location Service ◽  
Space Model

Location is the basis for the realization of location services, the integrity of the location information and its way of representation in indoor space model directly restricts the quality of location services. The construction of the existing indoor space model is mostly for specific applications and lack of uniform representation of location information. Several geospatial standards have been developed to meet the requirement of the indoor spatial information system, among which CityGML LOD4 and IndoorGML are the most relevant ones for indoor spatial information. However, from the perspective of Location Based Service (LBS), the CityGML LOD4 is more inclined to visualize the indoor space. Although IndoorGML is mainly used for indoor space navigation and has description (such as geometry, topology, and semantics) benefiting for indoor LBS, this standard model lack explicit representation of indoor location information. In this paper, from the perspective of Location Based Service (LBS), based on the IndoorGML standard, an indoor space location model (ISLM) conforming to human cognition is proposed through integration of the geometric and topological and semantic features of the indoor spatial entity. This model has the explicit description of location information which the standard indoor space model of IndoorGML and CityGML LOD4 does not have, which can lay the theoretical foundation for indoor location service such as indoor navigation, indoor routing and location query.

scholarly journals Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

2019 ◽  
Vol 9 (12) ◽  
pp. 2414 ◽  
Author(s):  
Wafa Njima ◽  
Rafik Zayani ◽  
Iness Ahriz ◽  
Michel Terre ◽  
Ridha Bouallegue
Keyword(s):  
Gradient Descent ◽  
Indoor Localization ◽  
Matrix Completion ◽  
Stochastic Gradient Descent ◽  
Mathematical Concepts ◽  
Localization Accuracy ◽  
Moment Estimation ◽  
Localization Scheme ◽  
Accelerated Gradient ◽  
The Internet Of Things

In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented and compared in terms of localization accuracy and complexity. Simulation results demonstrate that Adam outperforms all other algorithms in terms of localization accuracy and computational complexity.

scholarly journals A novel indoor localization scheme based on refined fingerprint-based autoencoder network

2018 ◽  
Vol 189 ◽  
pp. 03017
Author(s):  
Junhui Mei ◽  
Juntong Xi
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Access Point ◽  
Indoor Environments ◽  
Location Based Service ◽  
Accuracy Improvement ◽  
Positioning Systems ◽  
Localization Scheme ◽  
Environmental Properties ◽  
Indoor Scenes

Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
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.

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

Deep learning approach for UHF RFID-based indoor localization

2021 ◽  
pp. 1-13
Author(s):  
Haishu Ma ◽  
Zongzheng Ma ◽  
Lixia Li ◽  
Ya Gao
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Estimation Error ◽  
Research Direction ◽  
Linear Interpolation ◽  
Linear Mapping ◽  
Uhf Rfid ◽  
Indoor Location ◽  
Main Research

Due to the proliferation of the IoT devices, indoor location-based service is bringing huge business values and potentials. The positioning accuracy is restricted by the variability and complexity of the indoor environment. Radio Frequency Identification (RFID), as a key technology of the Internet of Things, has became the main research direction in the field of indoor positioning because of its non-contact, non-line-of-sight and strong anti-interference abilities. This paper proposes the deep leaning approach for RFID based indoor localization. Since the measured Received Signal Strength Indicator (RSSI) can be influenced by many indoor environment factors, Kalman filter is applied to erase the fluctuation. Furthermore, linear interpolation is adopted to increase the density of the reference tags. In order to improve the processing ability of the fingerprint database, deep neural network is adopted together with the fingerprinting method to optimize the non-linear mapping between fingerprints and indoor coordinates. The experimental results show that the proposed method achieves high accuracy with a mean estimation error of 0.347 m.

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