7,391 research outputs found
A new method for improving Wi-Fi based indoor positioning accuracy
Wi-Fi and smartphone based positioning technologies are play-ing a more and more important role in Location Based Service (LBS) indus-tries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the in-door positioning accuracy was developed. The new method initially used the Nearest Neighbor (NN) algorithm of the fingerprinting method to iden-tify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the pass loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated through differencing two model-derived distances from the same access point. The new method was tested and the results were compared and as-sessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy was improved to 3.4 m from 3.8 m of the NN algorithm
์ฌ๋ฌผ์ธํฐ๋ท์ ์ํ ๋ฌด์ ์ค๋ด ์ธก์ ์๊ณ ๋ฆฌ์ฆ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2022.2. ๊น์ฑ์ฒ .์ค๋ด ์์น ๊ธฐ๋ฐ ์๋น์ค๋ ์ค๋งํธํฐ์ ์ด์ฉํ ์ค๋ด์์์ ๊ฒฝ๋ก์๋ด, ์ค๋งํธ ๊ณต์ฅ์์์ ์์ ๊ด๋ฆฌ, ์ค๋ด ๋ก๋ด์ ์์จ์ฃผํ ๋ฑ ๋ง์ ๋ถ์ผ์ ์ ๋ชฉ๋ ์ ์์ผ๋ฉฐ, ์ฌ๋ฌผ์ธํฐ๋ท ์์ฉ์๋ ํ์์ ์ธ ๊ธฐ์ ์ด๋ค. ๋ค์ํ ์์น ๊ธฐ๋ฐ ์๋น์ค๋ฅผ ๊ตฌํํ๊ธฐ ์ํด์๋ ์ ํํ ์์น ์ ๋ณด๊ฐ ํ์ํ๋ฉฐ, ์ ํฉํ ๊ฑฐ๋ฆฌ ๋ฐ ์์น๋ฅผ ์ถ์ ๊ธฐ์ ์ด ํต์ฌ์ ์ด๋ค. ์ผ์ธ์์๋ ์์ฑํญ๋ฒ์์คํ
์ ์ด์ฉํด์ ์์น ์ ๋ณด๋ฅผ ํ๋ํ ์ ์๋ค.
๋ณธ ํ์๋
ผ๋ฌธ์์๋ ์์ดํ์ด ๊ธฐ๋ฐ ์ธก์ ๊ธฐ์ ์ ๋ํด ๋ค๋ฃฌ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก, ์ ํ์ ์ ํธ ์ธ๊ธฐ ๋ฐ ๋๋ฌ ์๊ฐ์ ์ด์ฉํ ์ ๋ฐํ ์ค๋ด ์์น ์ถ์ ์ ์ํ ์ธ ๊ฐ์ง ๊ธฐ์ ์ ๋ํด ๋ค๋ฃฌ๋ค. ๋จผ์ , ๋น๊ฐ์๊ฒฝ๋ก ํ๊ฒฝ์์์ ๊ฑฐ๋ฆฌ ์ถ์ ์ ํ๋๋ฅผ ํฅ์์์ผ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ ์ธก์์ ์ ํ๋๋ฅผ ํฅ์์ํค๋ ํ์ด๋ธ๋ฆฌ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์๋์ผ ๋ฐด๋ ๋์ญ์ ์ ํธ์ธ๊ธฐ๋ฅผ ๊ฐ์๋์ ์ธก์ ํ์ฌ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ ์ธก์ ๊ธฐ๋ฒ์ ์ ์ฉํ ๋, ๊ฑฐ๋ฆฌ ์ถ์ ๋ถ ๋จ๊ณ๋ง์ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ํ์ต์ ์ด์ฉํ ๊น์ ์ ๊ฒฝ๋ง ํ๊ท ๋ชจ๋ธ๋ก ๋์ฒดํ ๋ฐฉ์์ด๋ค. ์ ์ ํ ํ์ต๋ ๊น์ ํ๊ท ๋ชจ๋ธ์ ์ฌ์ฉ์ผ๋ก ๋น๊ฐ์๊ฒฝ๋ก ํ๊ฒฝ์์ ๋ฐ์ํ๋ ๊ฑฐ๋ฆฌ ์ถ์ ์ค์ฐจ๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๊ฐ์์ํฌ ์ ์์ผ๋ฉฐ, ๊ฒฐ๊ณผ์ ์ผ๋ก ์์น ์ถ์ ์ค์ฐจ ๋ํ ๊ฐ์์์ผฐ๋ค. ์ ์ํ ๋ฐฉ๋ฒ์ ์ค๋ด ๊ด์ ์ถ์ ๊ธฐ๋ฐ ๋ชจ์์คํ์ผ๋ก ํ๊ฐํ์ ๋, ๊ธฐ์กด ๊ธฐ๋ฒ๋ค์ ๋นํด์ ์์น ์ถ์ ์ค์ฐจ๋ฅผ ์ค๊ฐ๊ฐ์ ๊ธฐ์ค์ผ๋ก 22.3% ์ด์ ์ค์ผ ์ ์์์ ๊ฒ์ฆํ๋ค. ์ถ๊ฐ์ ์ผ๋ก, ์ ์ํ ๋ฐฉ๋ฒ์ ์ค๋ด์์์ AP ์์น๋ณํ ๋ฑ์ ๊ฐ์ธํจ์ ํ์ธํ๋ค.
๋ค์์ผ๋ก, ๋ณธ ๋
ผ๋ฌธ์์๋ ๋น๊ฐ์๊ฒฝ๋ก์์ ๋จ์ผ ๋์ญ ์์ ์ ํธ์ธ๊ธฐ๋ฅผ ์ธก์ ํ์ ๋ ๋น๊ฐ์๊ฒฝ๋ก๊ฐ ๋ง์ ์ค๋ด ํ๊ฒฝ์์ ์์น ์ถ์ ์ ํ๋๋ฅผ ๋์ด๊ธฐ ์ํ ๋ฐฉ์์ ์ ์ํ๋ค. ๋จ์ผ ๋์ญ ์์ ์ ํธ์ธ๊ธฐ๋ฅผ ์ด์ฉํ๋ ๋ฐฉ์์ ๊ธฐ์กด์ ์ด์ฉ๋๋ ์์ดํ์ด, ๋ธ๋ฃจํฌ์ค, ์ง๋น ๋ฑ์ ๊ธฐ๋ฐ์์ค์ ์ฝ๊ฒ ์ ์ฉ๋ ์ ์๊ธฐ ๋๋ฌธ์ ๋๋ฆฌ ์ด์ฉ๋๋ค. ํ์ง๋ง ์ ํธ ์ธ๊ธฐ์ ๋จ์ผ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ์ ์ด์ฉํ ๊ฑฐ๋ฆฌ ์ถ์ ์ ์๋นํ ์ค์ฐจ๋ฅผ ์ง๋
์ ์์น ์ถ์ ์ ํ๋๋ฅผ ๊ฐ์์ํจ๋ค. ์ด๋ฌํ ๋ฌธ์ ์ ์์ธ์ ๋จ์ผ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ๋ก๋ ์ค๋ด์์์ ๋ณต์กํ ์ ํ ์ฑ๋ ํน์ฑ์ ๋ฐ์ํ๊ธฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ด๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์ค๋ด ์์น ์ถ์ ์ ์ํ ๋ชฉ์ ์ผ๋ก, ์ค์ฒฉ๋ ๋ค์ค ์ํ ๊ฒฝ๋ก ๊ฐ์ ๋ชจ๋ธ์ ์๋กญ๊ฒ ์ ์ํ๋ค. ์ ์ํ ๋ชจ๋ธ์ ๊ฐ์๊ฒฝ๋ก ๋ฐ ๋น๊ฐ์๊ฒฝ๋ก์์์ ์ฑ๋ ํน์ฑ์ ๊ณ ๋ คํ์ฌ ์ ์ฌ์ ์ธ ํ๋ณด ์ํ๋ค์ ์ง๋๋ค. ํ ์๊ฐ์ ์์ ์ ํธ ์ธ๊ธฐ ์ธก์ ์น์ ๋ํด ๊ฐ ๊ธฐ์ค ๊ธฐ์ง๊ตญ๋ณ๋ก ์ต์ ์ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ ์ํ๋ฅผ ๊ฒฐ์ ํ๋ ํจ์จ์ ์ธ ๋ฐฉ์์ ์ ์ํ๋ค. ์ด๋ฅผ ์ํด ๊ธฐ์ง๊ตญ๋ณ ๊ฒฝ๋ก์์ค๋ชจ๋ธ ์ํ์ ์กฐํฉ์ ๋ฐ๋ฅธ ์ธก์ ๊ฒฐ๊ณผ๋ฅผ ํ๊ฐํ ์งํ๋ก์ ๋น์ฉํจ์๋ฅผ ์ ์ํ์๋ค. ๊ฐ ๊ธฐ์ง๊ตญ๋ณ ์ต์ ์ ์ฑ๋ ๋ชจ๋ธ์ ์ฐพ๋๋ฐ ํ์ํ ๊ณ์ฐ ๋ณต์ก๋๋ ๊ธฐ์ง๊ตญ ์์ ์ฆ๊ฐ์ ๋ฐ๋ผ ๊ธฐํ๊ธ์์ ์ผ๋ก ์ฆ๊ฐํ๋๋ฐ, ์ ์ ์๊ณ ๋ฆฌ์ฆ์ ์ด์ฉํ ํ์์ ์ ์ฉํ์ฌ ๊ณ์ฐ๋์ ์ต์ ํ์๋ค. ์ค๋ด ๊ด์ ์ถ์ ๋ชจ์์คํ์ ํตํ ๊ฒ์ฆ๊ณผ ์ค์ธก ๊ฒฐ๊ณผ๋ฅผ ์ด์ฉํ ๊ฒ์ฆ์ ์งํํ์์ผ๋ฉฐ, ์ ์ํ ๋ฐฉ์์ ์ค์ ์ค๋ด ํ๊ฒฝ์์ ๊ธฐ์กด์ ๊ธฐ๋ฒ๋ค์ ๋นํด ์์น ์ถ์ ์ค์ฐจ๋ฅผ ์ฝ 31% ๊ฐ์์์ผฐ์ผ๋ฉฐ ํ๊ท ์ ์ผ๋ก 1.92 m ์์ค์ ์ ํ๋๋ฅผ ๋ฌ์ฑํจ์ ํ์ธํ๋ค.
๋ง์ง๋ง์ผ๋ก FTM ํ๋กํ ์ฝ์ ์ด์ฉํ ์ค๋ด ์์น ์ถ์ ์๊ณ ๋ฆฌ์ฆ์ ๋ํด ์ฐ๊ตฌํ์๋ค. ์ค๋งํธํฐ์ ๋ด์ฅ ๊ด์ฑ ์ผ์์ ์์ดํ์ด ํต์ ์์ ์ ๊ณตํ๋ FTM ํ๋กํ ์ฝ์ ํตํ ๊ฑฐ๋ฆฌ ์ถ์ ์ ์ด์ฉํ์ฌ ์ค๋ด์์ ์ฌ์ฉ์์ ์์น๋ฅผ ์ถ์ ํ ์ ์๋ค. ํ์ง๋ง ์ค๋ด์ ๋ณต์กํ ๋ค์ค๊ฒฝ๋ก ํ๊ฒฝ์ผ๋ก ์ธํ ํผํฌ ๊ฒ์ถ ์คํจ๋ ๊ฑฐ๋ฆฌ ์ธก์ ์น์ ํธํฅ์ฑ์ ์ ๋ฐํ๋ค. ๋ํ ์ฌ์ฉํ๋ ๋๋ฐ์ด์ค์ ์ข
๋ฅ์ ๋ฐ๋ผ ์์์น ๋ชปํ ๊ฑฐ๋ฆฌ ์ค์ฐจ๊ฐ ๋ฐ์ํ ์์๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ค์ ํ๊ฒฝ์์ FTM ๊ฑฐ๋ฆฌ ์ถ์ ์ ์ด์ฉํ ๋ ๋ฐ์ํ ์ ์๋ ์ค์ฐจ๋ค์ ๊ณ ๋ คํ๊ณ ์ด๋ฅผ ๋ณด์ํ๋ ๋ฐฉ์์ ๋ํด ์ ์ํ๋ค. ํ์ฅ ์นผ๋ง ํํฐ์ ๊ฒฐํฉํ์ฌ FTM ๊ฒฐ๊ณผ๋ฅผ ์ฌ์ ํํฐ๋ง ํ์ฌ ์ด์๊ฐ์ ์ ๊ฑฐํ๊ณ , ๊ฑฐ๋ฆฌ ์ธก์ ์น์ ํธํฅ์ฑ์ ์ ๊ฑฐํ์ฌ ์์น ์ถ์ ์ ํ๋๋ฅผ ํฅ์์ํจ๋ค. ์ค๋ด์์์ ์คํ ๊ฒฐ๊ณผ ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ๊ฑฐ์น ์ธก์ ์น์ ํธํฅ์ฑ์ ์ฝ 44-65% ๊ฐ์์์ผฐ์ผ๋ฉฐ ์ต์ข
์ ์ผ๋ก ์ฌ์ฉ์์ ์์น๋ฅผ ์๋ธ๋ฏธํฐ๊ธ์ผ๋ก ์ถ์ ํ ์ ์์์ ๊ฒ์ฆํ๋ค.Indoor location-based services (LBS) can be combined with various applications such as indoor navigation for smartphone users, resource management in smart factories, and autonomous driving of robots. It is also indispensable for Internet of Things (IoT) applications. For various LBS, accurate location information is essential. Therefore, a proper ranging and positioning algorithm is important. For outdoors, the global navigation satellite system (GNSS) is available to provide position information. However, the GNSS is inappropriate indoors owing to the issue of the blocking of the signals from satellites. It is necessary to develop a technology that can replace GNSS in GNSS-denied environments. Among the various alternative systems, the one of promising technology is to use a Wi-Fi system that has already been applied to many commercial devices, and the infrastructure is in place in many regions.
In this dissertation, Wi-Fi based indoor localization methods are presented. In the specific, I propose the three major issues related to accurate indoor localization using received signal strength (RSS) and fine timing measurement (FTM) protocol in the 802.11 standard for my dissertation topics.
First, I propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. I replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error.
Next, I study on positioning algorithm that considering NLOS conditions for each APs, using single band RSS measurement. The single band RSS information is widely used for indoor localization because they can be easily implemented by using existing infrastructure like Wi-Fi, Blutooth, or Zigbee. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, I develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and NLOS states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, I formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, I apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92~m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09\% over the benchmark scenario.
Finally, I investigate accurate indoor tracking algorithm using FTM protocol in this dissertation.
By using the FTM ranging and the built-in sensors in a smartphone, it is possible to track the user's location in indoor. However, the failure of first peak detection due to the multipath effect causes a bias in the FTM ranging results in the practical indoor environment. Additionally, the unexpected ranging error dependent on device type also degrades the indoor positioning accuracy. In this study, I considered the factors of ranging error in the FTM protocol in practical indoor environment, and proposed a method to compensate ranging error. I designed an EKF-based tracking algorithm that adaptively removes outliers from the FTM result and corrects bias to increase positioning accuracy. The experimental results verified that the proposed algorithm reduces the average ofthe ranging bias by 43-65\% in an indoor scenarios, and can achieve the sub-meter accuracy in average route mean squared error of user's position in the experiment scenarios.Abstract i
Contents iv
List of Tables vi
List of Figures vii
1 INTRODUCTION 1
2 Hybrid Approach for Indoor Localization Using Received Signal Strength
of Dual-BandWi-Fi 6
2.1 Motivation 6
2.2 Preliminary 8
2.3 System model 11
2.4 Proposed Ranging Method 13
2.5 Performance Evaluation 16
2.5.1 Ray-Tracing-Based Simulation 16
2.5.2 Analysis of the Ranging Accuracy 21
2.5.3 Analysis of the Neural Network Structure 25
2.5.4 Analysis of Positioning Accuracy 26
2.6 Summary 29
3 Genetic Algorithm for Path Loss Model Selection in Signal Strength Based
Indoor Localization 31
3.1 Motivation 31
3.2 Preliminary 34
3.2.1 RSS-based Ranging Techniques 35
3.2.2 Positioning Technique 37
3.3 Proposed localization method 38
3.3.1 Localization Algorithm with Overlapped Multi-State Path Loss
Model 38
3.3.2 Localization with Genetic Algorithm-Based Search 41
3.4 Performance evaluation 46
3.4.1 Numerical simulation 50
3.4.2 Experimental results 56
3.5 Summary 60
4 Indoor User Tracking with Self-calibrating Range Bias Using FTM Protocol
62
4.1 Motivation 62
4.2 Preliminary 63
4.2.1 FTM ranging 63
4.2.2 PDR-based trajectory estimation 65
4.3 EKF design for adaptive compensation of ranging bias 66
4.4 Performance evaluation 69
4.4.1 Experimental scenario 69
4.4.2 Experimental results 70
4.5 Summary 75
5 Conclusion 76
Abstract (In Korean) 89๋ฐ
Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios
This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task
Design and realization of precise indoor localization mechanism for Wi-Fi devices
Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.Peer ReviewedPostprint (published version
Efficient AoA-based wireless indoor localization for hospital outpatients using mobile devices
The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatientsโ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatientsโ requirements for wayfinding
Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning
The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected
Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards
Time-of-flight, i.e., the time incurred by a signal to travel from
transmitter to receiver, is perhaps the most intuitive way to measure distances
using wireless signals. It is used in major positioning systems such as GPS,
RADAR, and SONAR. However, attempts at using time-of-flight for indoor
localization have failed to deliver acceptable accuracy due to fundamental
limitations in measuring time on Wi-Fi and other RF consumer technologies.
While the research community has developed alternatives for RF-based indoor
localization that do not require time-of-flight, those approaches have their
own limitations that hamper their use in practice. In particular, many existing
approaches need receivers with large antenna arrays while commercial Wi-Fi
nodes have two or three antennas. Other systems require fingerprinting the
environment to create signal maps. More fundamentally, none of these methods
support indoor positioning between a pair of Wi-Fi devices
without~third~party~support.
In this paper, we present a set of algorithms that measure the time-of-flight
to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these
algorithms and demonstrate a system that achieves accurate device-to-device
localization, i.e. enables a pair of Wi-Fi devices to locate each other without
any support from the infrastructure, not even the location of the access
points.Comment: 14 page
Distributed and adaptive location identification system for mobile devices
Indoor location identification and navigation need to be as simple, seamless,
and ubiquitous as its outdoor GPS-based counterpart is. It would be of great
convenience to the mobile user to be able to continue navigating seamlessly as
he or she moves from a GPS-clear outdoor environment into an indoor environment
or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing
infrastructure-based indoor localization systems lack such capability, on top
of potentially facing several critical technical challenges such as increased
cost of installation, centralization, lack of reliability, poor localization
accuracy, poor adaptation to the dynamics of the surrounding environment,
latency, system-level and computational complexities, repetitive
labor-intensive parameter tuning, and user privacy. To this end, this paper
presents a novel mechanism with the potential to overcome most (if not all) of
the abovementioned challenges. The proposed mechanism is simple, distributed,
adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a
mobile blind device can potentially utilize, as GPS-like reference nodes,
either in-range location-aware compatible mobile devices or preinstalled
low-cost infrastructure-less location-aware beacon nodes. The proposed approach
is model-based and calibration-free that uses the received signal strength to
periodically and collaboratively measure and update the radio frequency
characteristics of the operating environment to estimate the distances to the
reference nodes. Trilateration is then used by the blind device to identify its
own location, similar to that used in the GPS-based system. Simulation and
empirical testing ascertained that the proposed approach can potentially be the
core of future indoor and GPS-obstructed environments
Improving performance of pedestrian positioning by using vehicular communication signals
Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. In this paper, we propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) Channel state information instead of RSSI is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) Fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80cm when vehicle transmission period is 100ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency
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