4,214 research outputs found

    Distributed and adaptive location identification system for mobile devices

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    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

    Design and realization of precise indoor localization mechanism for Wi-Fi devices

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    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

    ์‚ฌ๋ฌผ์ธํ„ฐ๋„ท์„ ์œ„ํ•œ ๋ฌด์„  ์‹ค๋‚ด ์ธก์œ„ ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2022.2. ๊น€์„ฑ์ฒ .์‹ค๋‚ด ์œ„์น˜ ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค๋Š” ์Šค๋งˆํŠธํฐ์„ ์ด์šฉํ•œ ์‹ค๋‚ด์—์„œ์˜ ๊ฒฝ๋กœ์•ˆ๋‚ด, ์Šค๋งˆํŠธ ๊ณต์žฅ์—์„œ์˜ ์ž์› ๊ด€๋ฆฌ, ์‹ค๋‚ด ๋กœ๋ด‡์˜ ์ž์œจ์ฃผํ–‰ ๋“ฑ ๋งŽ์€ ๋ถ„์•ผ์— ์ ‘๋ชฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์‚ฌ๋ฌผ์ธํ„ฐ๋„ท ์‘์šฉ์—๋„ ํ•„์ˆ˜์ ์ธ ๊ธฐ์ˆ ์ด๋‹ค. ๋‹ค์–‘ํ•œ ์œ„์น˜ ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค๋ฅผ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ •ํ™•ํ•œ ์œ„์น˜ ์ •๋ณด๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ, ์ ํ•ฉํ•œ ๊ฑฐ๋ฆฌ ๋ฐ ์œ„์น˜๋ฅผ ์ถ”์ • ๊ธฐ์ˆ ์ด ํ•ต์‹ฌ์ ์ด๋‹ค. ์•ผ์™ธ์—์„œ๋Š” ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ์„ ์ด์šฉํ•ด์„œ ์œ„์น˜ ์ •๋ณด๋ฅผ ํš๋“ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ์™€์ดํŒŒ์ด ๊ธฐ๋ฐ˜ ์ธก์œ„ ๊ธฐ์ˆ ์— ๋Œ€ํ•ด ๋‹ค๋ฃฌ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์ „ํŒŒ์˜ ์‹ ํ˜ธ ์„ธ๊ธฐ ๋ฐ ๋„๋‹ฌ ์‹œ๊ฐ„์„ ์ด์šฉํ•œ ์ •๋ฐ€ํ•œ ์‹ค๋‚ด ์œ„์น˜ ์ถ”์ •์„ ์œ„ํ•œ ์„ธ ๊ฐ€์ง€ ๊ธฐ์ˆ ์— ๋Œ€ํ•ด ๋‹ค๋ฃฌ๋‹ค. ๋จผ์ €, ๋น„๊ฐ€์‹œ๊ฒฝ๋กœ ํ™˜๊ฒฝ์—์„œ์˜ ๊ฑฐ๋ฆฌ ์ถ”์ • ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผœ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ์ธก์œ„์˜ ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€๋“€์–ผ ๋ฐด๋“œ ๋Œ€์—ญ์˜ ์‹ ํ˜ธ์„ธ๊ธฐ๋ฅผ ๊ฐ์‡„๋Ÿ‰์„ ์ธก์ •ํ•˜์—ฌ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ์ธก์œ„ ๊ธฐ๋ฒ•์„ ์ ์šฉํ•  ๋•Œ, ๊ฑฐ๋ฆฌ ์ถ”์ •๋ถ€ ๋‹จ๊ณ„๋งŒ์„ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ํ•™์Šต์„ ์ด์šฉํ•œ ๊นŠ์€ ์‹ ๊ฒฝ๋ง ํšŒ๊ท€ ๋ชจ๋ธ๋กœ ๋Œ€์ฒดํ•œ ๋ฐฉ์•ˆ์ด๋‹ค. ์ ์ ˆํžˆ ํ•™์Šต๋œ ๊นŠ์€ ํšŒ๊ท€ ๋ชจ๋ธ์˜ ์‚ฌ์šฉ์œผ๋กœ ๋น„๊ฐ€์‹œ๊ฒฝ๋กœ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๊ฑฐ๋ฆฌ ์ถ”์ • ์˜ค์ฐจ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ฒฐ๊ณผ์ ์œผ๋กœ ์œ„์น˜ ์ถ”์ • ์˜ค์ฐจ ๋˜ํ•œ ๊ฐ์†Œ์‹œ์ผฐ๋‹ค. ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ์‹ค๋‚ด ๊ด‘์„ ์ถ”์  ๊ธฐ๋ฐ˜ ๋ชจ์˜์‹คํ—˜์œผ๋กœ ํ‰๊ฐ€ํ–ˆ์„ ๋•Œ, ๊ธฐ์กด ๊ธฐ๋ฒ•๋“ค์— ๋น„ํ•ด์„œ ์œ„์น˜ ์ถ”์ • ์˜ค์ฐจ๋ฅผ ์ค‘๊ฐ„๊ฐ’์„ ๊ธฐ์ค€์œผ๋กœ 22.3% ์ด์ƒ ์ค„์ผ ์ˆ˜ ์žˆ์Œ์„ ๊ฒ€์ฆํ–ˆ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ์‹ค๋‚ด์—์„œ์˜ AP ์œ„์น˜๋ณ€ํ™” ๋“ฑ์— ๊ฐ•์ธํ•จ์„ ํ™•์ธํ–ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„๊ฐ€์‹œ๊ฒฝ๋กœ์—์„œ ๋‹จ์ผ ๋Œ€์—ญ ์ˆ˜์‹ ์‹ ํ˜ธ์„ธ๊ธฐ๋ฅผ ์ธก์ •ํ–ˆ์„ ๋•Œ ๋น„๊ฐ€์‹œ๊ฒฝ๋กœ๊ฐ€ ๋งŽ์€ ์‹ค๋‚ด ํ™˜๊ฒฝ์—์„œ ์œ„์น˜ ์ถ”์ • ์ •ํ™•๋„๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•œ๋‹ค. ๋‹จ์ผ ๋Œ€์—ญ ์ˆ˜์‹ ์‹ ํ˜ธ์„ธ๊ธฐ๋ฅผ ์ด์šฉํ•˜๋Š” ๋ฐฉ์•ˆ์€ ๊ธฐ์กด์— ์ด์šฉ๋˜๋Š” ์™€์ดํŒŒ์ด, ๋ธ”๋ฃจํˆฌ์Šค, ์ง๋น„ ๋“ฑ์˜ ๊ธฐ๋ฐ˜์‹œ์„ค์— ์‰ฝ๊ฒŒ ์ ์šฉ๋  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋„๋ฆฌ ์ด์šฉ๋œ๋‹ค. ํ•˜์ง€๋งŒ ์‹ ํ˜ธ ์„ธ๊ธฐ์˜ ๋‹จ์ผ ๊ฒฝ๋กœ์†์‹ค ๋ชจ๋ธ์„ ์ด์šฉํ•œ ๊ฑฐ๋ฆฌ ์ถ”์ •์€ ์ƒ๋‹นํ•œ ์˜ค์ฐจ๋ฅผ ์ง€๋…€์„œ ์œ„์น˜ ์ถ”์ • ์ •ํ™•๋„๋ฅผ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์˜ ์›์ธ์€ ๋‹จ์ผ ๊ฒฝ๋กœ์†์‹ค ๋ชจ๋ธ๋กœ๋Š” ์‹ค๋‚ด์—์„œ์˜ ๋ณต์žกํ•œ ์ „ํŒŒ ์ฑ„๋„ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹ค๋‚ด ์œ„์น˜ ์ถ”์ •์„ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ, ์ค‘์ฒฉ๋œ ๋‹ค์ค‘ ์ƒํƒœ ๊ฒฝ๋กœ ๊ฐ์‡„ ๋ชจ๋ธ์„ ์ƒˆ๋กญ๊ฒŒ ์ œ์‹œํ•œ๋‹ค. ์ œ์•ˆํ•œ ๋ชจ๋ธ์€ ๊ฐ€์‹œ๊ฒฝ๋กœ ๋ฐ ๋น„๊ฐ€์‹œ๊ฒฝ๋กœ์—์„œ์˜ ์ฑ„๋„ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ž ์žฌ์ ์ธ ํ›„๋ณด ์ƒํƒœ๋“ค์„ ์ง€๋‹Œ๋‹ค. ํ•œ ์ˆœ๊ฐ„์˜ ์ˆ˜์‹  ์‹ ํ˜ธ ์„ธ๊ธฐ ์ธก์ •์น˜์— ๋Œ€ํ•ด ๊ฐ ๊ธฐ์ค€ ๊ธฐ์ง€๊ตญ๋ณ„๋กœ ์ตœ์ ์˜ ๊ฒฝ๋กœ์†์‹ค ๋ชจ๋ธ ์ƒํƒœ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ํšจ์œจ์ ์ธ ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ธฐ์ง€๊ตญ๋ณ„ ๊ฒฝ๋กœ์†์‹ค๋ชจ๋ธ ์ƒํƒœ์˜ ์กฐํ•ฉ์— ๋”ฐ๋ฅธ ์ธก์œ„ ๊ฒฐ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•  ์ง€ํ‘œ๋กœ์„œ ๋น„์šฉํ•จ์ˆ˜๋ฅผ ์ •์˜ํ•˜์˜€๋‹ค. ๊ฐ ๊ธฐ์ง€๊ตญ๋ณ„ ์ตœ์ ์˜ ์ฑ„๋„ ๋ชจ๋ธ์„ ์ฐพ๋Š”๋ฐ ํ•„์š”ํ•œ ๊ณ„์‚ฐ ๋ณต์žก๋„๋Š” ๊ธฐ์ง€๊ตญ ์ˆ˜์˜ ์ฆ๊ฐ€์— ๋”ฐ๋ผ ๊ธฐํ•˜๊ธ‰์ˆ˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๋Š”๋ฐ, ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•œ ํƒ์ƒ‰์„ ์ ์šฉํ•˜์—ฌ ๊ณ„์‚ฐ๋Ÿ‰์„ ์–ต์ œํ•˜์˜€๋‹ค. ์‹ค๋‚ด ๊ด‘์„ ์ถ”์  ๋ชจ์˜์‹คํ—˜์„ ํ†ตํ•œ ๊ฒ€์ฆ๊ณผ ์‹ค์ธก ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•œ ๊ฒ€์ฆ์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ์ œ์•ˆํ•œ ๋ฐฉ์•ˆ์€ ์‹ค์ œ ์‹ค๋‚ด ํ™˜๊ฒฝ์—์„œ ๊ธฐ์กด์˜ ๊ธฐ๋ฒ•๋“ค์— ๋น„ํ•ด ์œ„์น˜ ์ถ”์ • ์˜ค์ฐจ๋ฅผ ์•ฝ 31% ๊ฐ์†Œ์‹œ์ผฐ์œผ๋ฉฐ ํ‰๊ท ์ ์œผ๋กœ 1.92 m ์ˆ˜์ค€์˜ ์ •ํ™•๋„๋ฅผ ๋‹ฌ์„ฑํ•จ์„ ํ™•์ธํ–ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ FTM ํ”„๋กœํ† ์ฝœ์„ ์ด์šฉํ•œ ์‹ค๋‚ด ์œ„์น˜ ์ถ”์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์Šค๋งˆํŠธํฐ์˜ ๋‚ด์žฅ ๊ด€์„ฑ ์„ผ์„œ์™€ ์™€์ดํŒŒ์ด ํ†ต์‹ ์—์„œ ์ œ๊ณตํ•˜๋Š” FTM ํ”„๋กœํ† ์ฝœ์„ ํ†ตํ•œ ๊ฑฐ๋ฆฌ ์ถ”์ •์„ ์ด์šฉํ•˜์—ฌ ์‹ค๋‚ด์—์„œ ์‚ฌ์šฉ์ž์˜ ์œ„์น˜๋ฅผ ์ถ”์ ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์‹ค๋‚ด์˜ ๋ณต์žกํ•œ ๋‹ค์ค‘๊ฒฝ๋กœ ํ™˜๊ฒฝ์œผ๋กœ ์ธํ•œ ํ”ผํฌ ๊ฒ€์ถœ ์‹คํŒจ๋Š” ๊ฑฐ๋ฆฌ ์ธก์ •์น˜์— ํŽธํ–ฅ์„ฑ์„ ์œ ๋ฐœํ•œ๋‹ค. ๋˜ํ•œ ์‚ฌ์šฉํ•˜๋Š” ๋””๋ฐ”์ด์Šค์˜ ์ข…๋ฅ˜์— ๋”ฐ๋ผ ์˜ˆ์ƒ์น˜ ๋ชปํ•œ ๊ฑฐ๋ฆฌ ์˜ค์ฐจ๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ค์ œ ํ™˜๊ฒฝ์—์„œ FTM ๊ฑฐ๋ฆฌ ์ถ”์ •์„ ์ด์šฉํ•  ๋•Œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์˜ค์ฐจ๋“ค์„ ๊ณ ๋ คํ•˜๊ณ  ์ด๋ฅผ ๋ณด์ƒํ•˜๋Š” ๋ฐฉ์•ˆ์— ๋Œ€ํ•ด ์ œ์‹œํ•œ๋‹ค. ํ™•์žฅ ์นผ๋งŒ ํ•„ํ„ฐ์™€ ๊ฒฐํ•ฉํ•˜์—ฌ FTM ๊ฒฐ๊ณผ๋ฅผ ์‚ฌ์ „ํ•„ํ„ฐ๋ง ํ•˜์—ฌ ์ด์ƒ๊ฐ’์„ ์ œ๊ฑฐํ•˜๊ณ , ๊ฑฐ๋ฆฌ ์ธก์ •์น˜์˜ ํŽธํ–ฅ์„ฑ์„ ์ œ๊ฑฐํ•˜์—ฌ ์œ„์น˜ ์ถ”์  ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. ์‹ค๋‚ด์—์„œ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ฑฐ์น˜ ์ธก์ •์น˜์˜ ํŽธํ–ฅ์„ฑ์„ ์•ฝ 44-65% ๊ฐ์†Œ์‹œ์ผฐ์œผ๋ฉฐ ์ตœ์ข…์ ์œผ๋กœ ์‚ฌ์šฉ์ž์˜ ์œ„์น˜๋ฅผ ์„œ๋ธŒ๋ฏธํ„ฐ๊ธ‰์œผ๋กœ ์ถ”์ ํ•  ์ˆ˜ ์žˆ์Œ์„ ๊ฒ€์ฆํ–ˆ๋‹ค.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•

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    RFID Localisation For Internet Of Things Smart Homes: A Survey

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    The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

    AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

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    With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

    Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

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    The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
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