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 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 IoT Enabled BLE and LoRa based Indoor Localization without GPS

2021 ◽  
Vol 12 (4) ◽  
pp. 1637-1651
Author(s):  
P Kanakaraja, Et. al.
Keyword(s):  
Open Space ◽  
Indoor Localization ◽  
Low Energy ◽  
Gps Tracking ◽  
Location Tracking ◽  
Gps Receiver ◽  
Indoor Location ◽  
The World ◽  
Exact Position ◽  
The Common

The common problem that people face these days to find out the Indoor location of a particular Object or a Person exactly. GPS-based location tracking is one of the very important services nowadays. To use GPS tracking to find a path to our destination and also track the position of our goods using GPS Tracking. It is also possible to track the exact location in absence of GPS with the help of proposed implementation. In some cases like indoor location, the GPS tracker cannot locate exact position of a person or an object. GPS Tracker generally requires an open space with no roof on it. Bluetooth Low Energy (BLE) iBeacons that have to understand basically where this technology can be used or where it is useful. Basically, it is for an especially indoor location tracking of something movable or mobile without the use of a GPS Receiver. This proposed article is going to discuss the idea of making an Indoor location tracker using BLE and LoRa Technology along with IoT Dashboard through “The Things Network” (TTN) to know the position of the object or a person anywhere in the world

An Improved Indoor Location Technique using Kalman Filter

2018 ◽  
Vol 7 (2.14) ◽  
pp. 1 ◽  
Author(s):  
Nik Fariz ◽  
Norziana Jamil ◽  
Marina Md Din ◽  
Mohd Ezanee Rusli ◽  
Zahrah Sharudin ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Noise Signal ◽  
Space Environment ◽  
Location Tracking ◽  
Received Signal Strength Indicator ◽  
Indoor Location ◽  
Positioning Technique ◽  
Do So

Indoor positioning technique is used to trace location of entities within a non-space environment riding from the incapability of GPS to do so. Most of indoor localization techniques proposed by researchers aimed at discovering an optimized solution for indoor location tracking with high precision and accuracy. This paper proposes an improved indoor location technique by implementing Trilateration and Kalman Filter technique that can manipulate noise signal deduced from raw Received Signal Strength Indicator (RSSI). Upon implementing the technique, observation and comparison are made to measure the effectiveness and reliability of the enhanced Kalman Filter in tracking indoor positioning. Our analysis and finding shows that the enhanced indoor positioning technique improves the accuracy significantly.  

scholarly journals Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 532
Author(s):  
Henglin Pu ◽  
Chao Cai ◽  
Menglan Hu ◽  
Tianping Deng ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Indoor Localization ◽  
Weighting Function ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Sound Source Localization ◽  
Angle Of Arrival ◽  
Sound Sources ◽  
Localization Algorithms

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

scholarly journals DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2000
Author(s):  
Marius Laska ◽  
Jörg Blankenbach
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Success Probability ◽  
Local Area ◽  
Position Estimation ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Area Network ◽  
Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

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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