scholarly journals Determination of an Indoor Target Position: An Accurate and Adaptable Approach Based on Differential Positioning

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Siye Wang ◽  
Chang Ding ◽  
Weiqing Huang ◽  
Yanfang Zhang ◽  
Jianguo Jiang ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Target Position ◽  
Signal Strength ◽  
Small Scale ◽  
Localization Algorithm ◽  
Accurate Identification ◽  
Differential Signal ◽  
Wireless Signal ◽  
Communication Environments ◽  
The Time Domain

The growing demand for new products that rely on the accurate identification of a target’s location indoors, while remaining mindful of cost, continues to drive research in this important and challenging area. Researchers are actively pursuing algorithmic improvements to eliminate errors introduced from complex interference factors present in indoor, wireless communication environments. In this work, we adopt a differential signal strength method in the design of our new indoor localization algorithm. The proposed algorithm reduces errors in the time domain by smoothing out the wireless signal fluctuations, thus stabilizing the signal; a single exponential algorithm is applied to the signal strength parameters collected to accomplish this. The target’s position is then computed by utilizing both the plane geometric method and difference localization theory. This combination of techniques is reasonable for the environment under consideration (small scale, wireless), as the multipath effects for the signal are approximately equal under these conditions. In addition, the proposed approach is compatible with a wide variety of technologies (e.g., RFID and Bluetooth); it can be cost-effectively deployed by leveraging an existing hardware infrastructure. The proposed approach has been implemented and experimentally validated. The test results are very promising: they indicate that our algorithm improves the positioning accuracy by 70%–80% in comparison with the trilateration and LANDMARC positioning algorithms.

scholarly journals SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881563 ◽  
Author(s):  
Jie Wei ◽  
Fang Zhao ◽  
Haiyong Luo
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Based Services ◽  
Shopping Mall ◽  
Time Stamp ◽  
Localization Algorithm ◽  
Variation Pattern ◽  
Localization Accuracy ◽  
User Location

With the development of indoor localization technology, the location-based services such as product advertising recommendation in the shopping mall attract widespread attention, as precise user location significantly improves the efficiency of advertising push and brings broader profits. However, most of the Wi-Fi-based indoor localization approaches requiring professionals to deploy expensive beacon devices and intensively collect fingerprints in each location grid, which severely limits its extensive promotion. We introduce a zero-cost indoor localization algorithm utilizing crowdsourcing fingerprints to obtain the shop recognition where the user is located. Naturally utilizing the Wi-Fi, GPS, and time-stamp fingerprints collected from the smartphone when user paid as the crowdsourcing fingerprint, we avoid the requirement for indoor map and get rid of both devices cost and manual signal collecting process. Moreover, a shop-level hierarchical indoor localization framework is proposed, and high robustness features based on Wi-Fi sequences variation pattern in the same shop analysis are designed to avoid the received signal strength fluctuations. Besides, we also pay more attention to mine the popularity properties of shops and explore GPS features to improve localization accuracy in the Wi-Fi absence situation effectively. Massive experiments indicate that SP-Loc achieves more than 93% localization accuracy.

scholarly journals An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2857
Author(s):  
Simon Tomažič ◽  
Igor Škrjanc
Keyword(s):  
Data Acquisition ◽  
Indoor Localization ◽  
Low Cost ◽  
Path Loss ◽  
Signal Strength ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization System ◽  
Good Trade ◽  
Strength Modeling

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.

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.

The Location Algorithm Based on Square-Root Cubature Kalman Filter

2013 ◽  
Vol 325-326 ◽  
pp. 1525-1529
Author(s):  
Ying Liu ◽  
Jun Feng Su ◽  
Ming Qiang Zhu
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Signal Strength ◽  
Estimation Algorithm ◽  
Received Signal Strength ◽  
Square Root ◽  
Received Signal Strength Indicator ◽  
Cubature Kalman Filter ◽  
Wireless Signal ◽  
Parameter Estimation Algorithm

When wireless signal is used for indoor localization, there is no consistent relationship between signal strength received by the receiving nodes and distance from the receiving nodes to the receiving nodes, so there is a larger localization error for the Received Signal Strength Indication (RSSI) in the indoor environment. A new received signal strength indicator parameter estimation algorithm based on square-root cubature kalman filter is proposed in this paper, this algorithm utilizes Square-root Cubature Kalman filter (SCKF) to estimate the target’s position and the RSSI channel attenuation parameter simultaneously. The experimental results demonstrate that there is a better accuracy for the algorithm based on SCKF than the traditional method.

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