385 research outputs found

    Localization in GPS denied environment

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    Robust Positioning in the Presence of Multipath and NLOS GNSS Signals

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    GNSS signals can be blocked and reflected by nearby objects, such as buildings, walls, and vehicles. They can also be reflected by the ground and by water. These effects are the dominant source of GNSS positioning errors in dense urban environments, though they can have an impact almost anywhere. Non- line-of-sight (NLOS) reception occurs when the direct path from the transmitter to the receiver is blocked and signals are received only via a reflected path. Multipath interference occurs, as the name suggests, when a signal is received via multiple paths. This can be via the direct path and one or more reflected paths, or it can be via multiple reflected paths. As their error characteristics are different, NLOS and multipath interference typically require different mitigation techniques, though some techniques are applicable to both. Antenna design and advanced receiver signal processing techniques can substantially reduce multipath errors. Unless an antenna array is used, NLOS reception has to be detected using the receiver's ranging and carrier-power-to-noise-density ratio (C/N0) measurements and mitigated within the positioning algorithm. Some NLOS mitigation techniques can also be used to combat severe multipath interference. Multipath interference, but not NLOS reception, can also be mitigated by comparing or combining code and carrier measurements, comparing ranging and C/N0 measurements from signals on different frequencies, and analyzing the time evolution of the ranging and C/N0 measurements

    A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

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    Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

    Probabilistic Time of Arrival Localization

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    In this letter, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that bias our solutions, and that we can rely on a probabilistic model to learn and compensate for them. The resulting localization error is validated using measurements from a live LTE cellular network to be less than 10 meters, representing an order-of-magnitude improvement

    ์‚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•

    Smart Passive Localization Using Time Difference of Arrival

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    A smart passive localization system using time difference of arrival (TDoA) measurements is designed and analyzed with the goal of providing the position information for the construction of frequency allocation maps

    Accurate Range-based Indoor Localization Using PSO-Kalman Filter Fusion

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    Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion (PKFF) is proposed that minimizes the effects of NLOS on localization error without using additional sensors or profiling. Location estimates from a windowed Particle Swarm Optimization (PSO) and a dynamically adjusted KF are fused based on a weighted variance factor. PKFF achieved a 40% lower 90-percentile root-mean-square localization error (RMSE) over the standard least squares trilateration algorithm at 61 cm compared to 102 cm
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