170 research outputs found

    A closed-loop EKF and multi-failure diagnosis approach for cooperative GNSS positioning

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    Current cooperative positioning with Global Navigation Satellite System (GNSS) for connected vehicle application mainly uses pseudorange measurements. However the positioning accuracy offered cannot meet the requirements for lane-level positioning, collision avoidance and future automatic driving, which needs real-time positioning accuracy of better than 0.5m. Furthermore, there is an apparent lack of research into the integrity issue for these new applications under emerging driverless vehicle applications. In order to overcome those problems, a new Extended Kalman Filter (EKF) and a multi-failure diagnosis algorithm are developed to process both GNSS pseudorange and carrier phase measurements. We first introduce a new closed-loop EKF with partial ambiguity resolution (PAR) as feedback to address the low accuracy issue. Then a multi-failure diagnosis algorithm is proposed to improve integrity and reliability. The core of this new algorithm includes using Carrier phase based Receiver Autonomous Integrity Monitoring (CRAIM) method for failure detection, and the double extended w-test detectors to identify failure. A cooperative positioning experiment was carried out to validate the proposed method. The results show that the proposed closed-loop EKF can provide highly accurate positioning, and the multi-failure diagnosis method is effective in detecting and identifying failures for both code and carrier phase measurements

    Adaptive filtering applications to satellite navigation

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    PhDDifferential Global Navigation Satellite Systems employ the extended Kalman filter to estimate the reference position error. High accuracy integrated navigation systems have the ability to mix traditional inertial sensor outputs with navigation satellite based position information and can be used to develop high accuracy landing systems for aircraft. This thesis considers a host of estimation problems associated with aircraft navigation systems that currently rely on the extended Kalman filter and proposes to use a nonlinear estimation algorithm, the unscented Kalman filter (UKF) that does not rely on Jacobian linearisation. The objective is to develop high accuracy positioning algorithms to facilitate the use of GNSS or DGNSS for aircraft landing. Firstly, the position error in a typical satellite navigation problem depends on the accuracy of the orbital ephemeris. The thesis presents results for the prediction of the orbital ephemeris from a customised navigation satellite receiver's data message. The SDP4/SDP8 algorithms and suitable noise models are used to establish the measured data. Secondly, the differential station common mode position error not including the contribution due to errors in the ephemeris is usually estimated by employing an EKF. The thesis then considers the application of the UKF to the mixing problem, so as to facilitate the mixing of measurements made by either a GNSS or a DGNSS and a variety of low cost or high-precision INS sensors. Precise, adaptive UKFs and a suitable nonlinear propagation method are used to estimate the orbit ephemeris and the differential position and the navigation filter mixing errors. The results indicate the method is particularly suitable for estimating the orbit ephemeris of navigation satellites and the differential position and navigation filter mixing errors, thus facilitating interoperable DGNSS operation for aircraft landing

    ์„ผํ‹ฐ๋ฏธํ„ฐ ๊ธ‰ ๊ด‘์—ญ ๋ณด๊ฐ•ํ•ญ๋ฒ• ์‹œ์Šคํ…œ์˜ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜ ๋ณด์ •์ •๋ณด ์ƒ์„ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ๊ธฐ์ฐฝ๋ˆ.Recently, the demand for high-precision navigation systems for centimeter-level service has been growing rapidly for various Global Navigation Satellite System (GNSS) applications. The network Real-Time Kinematic (RTK) is one of the candidate solution to provide high-accuracy position to user in real-time. However, the network RTK requires a lot of reference stations for nationwide service. Furthermore, it requires high-speed data-link for broadcasting their scalar-type corrections. This dissertation proposed a new concept of satellite augmentation system called Compact Wide-Area RTK, which provides centimeter-level positioning service on national or continental scales to overcoming the limitation of the legacy network RTK methods. Using the wide-area network of multiple reference stations whose distance is 200~1,000 km, the proposed system generates three types of carrier-phase-based corrections: satellite orbit corrections, satellite code/phase clock (CPC) corrections, tropospheric corrections. Through the strategy of separating the scalar-type corrections of network RTK into vector forms of each error component, it is enable to expand network RTK coverage to continental scale using a similar number of reference stations as legacy meter-level Satellite-Based Augmentation System (SBAS). Furthermore, it is possible to broadcast their corrections over a wide-area using geosynchronous (GEO) satellite with extremely low-speed datalink of 250 bps likewise of legacy SBAS. To sum up, the proposed system can improve position accuracy by centimeter-level while maintaining the hardware infrastructure of the meter-level legacy SBAS. This study mainly discussed on the overall system architecture and core algorithms for generating satellite CPC corrections and tropospheric corrections. This study proposed a new Three-Carrier Ambiguity Resolution (TCAR) algorithm using ionosphere-free combinations to correctly solve the integer ambiguity in wide-area without any ionospheric corrections. The satellite CPC corrections are calculated based on multiple stations for superior and robust performance under communication delay and outage. The proposed algorithm dramatically reduced the latency compensation errors and message amounts with compare to conventional RTK protocols. The tropospheric corrections of the compact wide-area RTK system are computed using GPS-estimated precise tropospheric delay and weather data based model together. The proposed algorithm adopts spherical harmonics function to significantly reduce the message amounts and required number of GPS reference stations than the network RTK and Precise Point Positioning-RTK (PPP-RTK), while accurately modeling the spatial characteristic of tropospheric delay with weather data together. In order to evaluate the user domain performance of the compact wide-area RTK system, this study conducted the feasibility test on mid-west and south USA using actual GPS measurements. As a result, the 95% horizontal position error is about 1.9 cm and the 95% vertical position error is 7.0 cm after the integer ambiguity is correctly fixed using GPS-only signals. The user ambiguity resolution takes about 2 minutes, and success-fix rate is about 100 % when stable tropospheric condition. In conclusion, the compact wide-area RTK system can provide centimeter-level positioning service to wide-area coverage with extremely low-speed data link via GEO satellite. We hope that this new system will consider as candidate solution for nationwide centimeter-level service such as satellite augmentation system of the Korea Positioning System (KPS).์ตœ๊ทผ ์ž์œจ์ฃผํ–‰์ž๋™์ฐจ, ๋ฌด์ธ ๋“œ๋ก  ๋ฐฐ์†ก, ์ถฉ๋Œ ํšŒํ”ผ, ๋ฌด์ธํŠธ๋ž™ํ„ฐ๋ฅผ ์ด์šฉํ•œ ์Šค๋งˆํŠธ ๋ฌด์ธ ๊ฒฝ์ž‘ ๋“ฑ ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ(GNSS, Global Navigation Satellite System)์„ ์‚ฌ์šฉํ•˜๋Š” ๋‹ค์–‘ํ•œ ์‘์šฉ๋ถ„์•ผ์—์„œ ์ˆ˜ cm ์ˆ˜์ค€์˜ ์ •๋ฐ€ ์œ„์น˜ ์ •๋ณด์— ๋Œ€ํ•œ ์š”๊ตฌ๊ฐ€ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” 1 m ๊ธ‰์˜ ์ •ํ™•ํ•˜๊ณ  ์‹ ๋ขฐ์„ฑ ๋†’์€ ์œ„์น˜ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ธฐ์กด์˜ ์ •์ง€๊ถค๋„์œ„์„ฑ ๊ธฐ๋ฐ˜ ๊ด‘์—ญ ๋ณด๊ฐ•ํ•ญ๋ฒ• ์‹œ์Šคํ…œ(SBAS, Satellite-Based Augmentation System)์˜ ๊ธฐ์ค€๊ตญ ์ธํ”„๋ผ๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ ํ•ญ๋ฒ• ์„ฑ๋Šฅ์„ ์ˆ˜ cm ์ˆ˜์ค€์œผ๋กœ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜์˜ ์ดˆ์ •๋ฐ€ ๋ณด์ •์ •๋ณด ์ƒ์„ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์‹ค์‹œ๊ฐ„ ์ •๋ฐ€ ์ธก์œ„(RTK, Real-Time Kinematic)๋Š” ๋ฐ˜์†กํŒŒ ์œ„์ƒ ์ธก์ •์น˜์— ํฌํ•จ๋œ ๋ฏธ์ง€์ •์ˆ˜๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๊ฒฐ์ •ํ•˜์—ฌ ์ˆ˜ cm ์ˆ˜์ค€์˜ ์ •๋ฐ€ ํ•ญ๋ฒ• ์„œ๋น„์Šค๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ๋Œ€ํ‘œ์ ์ธ ๊ธฐ๋ฒ•์ด๋‹ค. ๊ทธ ์ค‘์—์„œ๋„ ์•ฝ 50~70 km ๊ฐ„๊ฒฉ์œผ๋กœ ๋ถ„ํฌ๋œ ๋‹ค์ˆ˜์˜ ๊ธฐ์ค€๊ตญ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜๋Š” Network RTK ๊ธฐ๋ฒ•์€ ๋™์  ์‚ฌ์šฉ์ž์˜ ๋น ๋ฅด๊ณ  ์ •ํ™•ํ•œ ์œ„์น˜ ๊ฒฐ์ •์ด ๊ฐ€๋Šฅํ•œ ์ธํ”„๋ผ๋กœ์„œ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์Šค์นผ๋ผ ํ˜•ํƒœ๋กœ ๊ตฌ์„ฑ๋œ Network RTK ๋ณด์ •์ •๋ณด๋Š” ๊ฐ ๊ธฐ์ค€๊ตญ ๋ณ„๋กœ ๊ด€์ธก๋œ ์œ„์„ฑ ์ˆ˜์— ๋”ฐ๋ผ ์ƒ์„ฑ์ด ๋˜๊ธฐ ๋•Œ๋ฌธ์— ๋ณด์ • ๋ฐ์ดํ„ฐ ๋Ÿ‰์ด ์ƒ๋‹นํžˆ ๋ฐฉ๋Œ€ํ•˜๋‹ค. ๋ฉ”์‹œ์ง€ ์ „์†ก์— ํ•„์š”ํ•œ ๋ฐ์ดํ„ฐ ๋Ÿ‰์ด ๋งŽ์„์ˆ˜๋ก ๊ณ ์†์˜ ํ†ต์‹  ํ™˜๊ฒฝ์„ ํ•„์š”๋กœ ํ•˜๋ฉฐ, ๋ฉ”์‹œ์ง€ ์‹œ๊ฐ„ ์ง€์—ฐ์ด๋‚˜ ํ†ต์‹  ๋‹จ์ ˆ์— ๋งค์šฐ ์ทจ์•ฝํ•œ ๋ฌธ์ œ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ์Šค์นผ๋ผ ํ˜•ํƒœ์˜ ๋ณด์ •์ •๋ณด๋Š” ์‚ฌ์šฉ์ž์™€ ๊ธฐ์ค€๊ตญ ๊ฐ„์˜ ๊ฑฐ๋ฆฌ๊ฐ€ ๋ฉ€์–ด์งˆ์ˆ˜๋ก ๋ณด์ • ์˜ค์ฐจ๊ฐ€ ํฌ๊ฒŒ ๋ฐœ์ƒํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋Œ€๋ฅ™ ํ˜น์€ ๋‚˜๋ผ ๊ทœ๋ชจ์˜ ๊ด‘์—ญ์—์„œ ์„œ๋น„์Šคํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ˆ˜์‹ญ~์ˆ˜๋ฐฑ ๊ฐœ ์ด์ƒ์˜ ๊ธฐ์ค€๊ตญ ์ธํ”„๋ผ ๊ตฌ์ถ•์ด ํ•„์ˆ˜์ ์ด๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, SBAS๊ฐ€ ํ•œ๋ฐ˜๋„ ์ง€์—ญ ์„œ๋น„์Šค๋ฅผ ์œ„ํ•ด 5~7๊ฐœ์˜ ๊ธฐ์ค€๊ตญ์ด ํ•„์š”ํ•œ ๋ฐ˜๋ฉด Network RTK๋Š” 90~100๊ฐœ์˜ ๊ธฐ์ค€๊ตญ์ด ํ•„์š”ํ•˜๋‹ค. ์ฆ‰ Network RTK๋Š” ์‹œ์Šคํ…œ ๊ตฌ์ถ• ๋ฐ ์œ ์ง€ ๋น„์šฉ์ด SBAS ๋Œ€๋น„ ์•ฝ 15๋ฐฐ ์ •๋„ ๋งŽ์ด ๋“ค๊ฒŒ ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด Network RTK์˜ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๋Œ€๋ฅ™ ๊ธ‰ ๊ด‘๋ฒ”์œ„ํ•œ ์˜์—ญ์—์„œ ์‹ค์‹œ๊ฐ„์œผ๋กœ cm๊ธ‰ ์ดˆ์ •๋ฐ€ ์œ„์น˜๊ฒฐ์ • ์„œ๋น„์Šค ์ œ๊ณต์ด ๊ฐ€๋Šฅํ•œ Compact Wide-Area RTK ๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์˜ ๊ด‘์—ญ๋ณด๊ฐ•ํ•ญ๋ฒ•์‹œ์Šคํ…œ ์•„ํ‚คํ…์ฒ˜๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. Compact Wide-Area RTK๋Š” ์•ฝ 200~1,000 km ๊ฐ„๊ฒฉ์œผ๋กœ ๋„“๊ฒŒ ๋ถ„ํฌ๋œ ๊ธฐ์ค€๊ตญ ๋„คํŠธ์›Œํฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜์˜ ์ •๋ฐ€ํ•œ ์œ„์„ฑ ๊ถค๋„ ๋ณด์ •์ •๋ณด, ์œ„์„ฑ Code/Phase ์‹œ๊ณ„ ๋ณด์ •์ •๋ณด, ๋Œ€๋ฅ˜์ธต ๋ณด์ •์ •๋ณด๋ฅผ ์ƒ์„ฑํ•˜๋Š” ์‹œ์Šคํ…œ์ด๋‹ค. ๊ธฐ์กด ์Šค์นผ๋ผ ํ˜•ํƒœ์˜ Network RTK ๋ณด์ •์ •๋ณด ๋Œ€์‹  ์˜ค์ฐจ ์š”์†Œ ๋ณ„ ๋ฒกํ„ฐ ํ˜•ํƒœ์˜ ์ •๋ฐ€ ๋ณด์ •์ •๋ณด๋ฅผ ์ƒ์„ฑํ•จ์œผ๋กœ์จ ๋ฐ์ดํ„ฐ ๋Ÿ‰์„ ํš๊ธฐ์ ์œผ๋กœ ์ ˆ๊ฐํ•˜๊ณ  ์„œ๋น„์Šค ์˜์—ญ์„ ํ™•์žฅํ•  ์ˆ˜ ์žˆ๋‹ค. ์ตœ์ข…์ ์œผ๋กœ SBAS์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ 250 bps์˜ ์ €์† ํ†ต์‹  ๋งํฌ๋ฅผ ๊ฐ€์ง„ ์ •์ง€๊ถค๋„์œ„์„ฑ์„ ํ†ตํ•ด ๊ด‘์—ญ์œผ๋กœ ๋ณด์ •์ •๋ณด ๋ฐฉ์†ก์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” 3๊ฐ€์ง€ ๋ณด์ •์ •๋ณด ์ค‘ ์œ„์„ฑ Code/Phase ์‹œ๊ณ„ ๋ณด์ •์ •๋ณด์™€ ๋Œ€๋ฅ˜์ธต ๋ณด์ •์ •๋ณด ์ƒ์„ฑ์„ ์œ„ํ•œ ํ•ต์‹ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•ด ์ค‘์ ์ ์œผ๋กœ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜์˜ ์ •๋ฐ€ ๋ณด์ •์ •๋ณด ์ƒ์„ฑ์„ ์œ„ํ•ด์„œ๋Š” ๋จผ์ € ๋ฏธ์ง€์ •์ˆ˜๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๊ฒฐ์ •ํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‚ผ์ค‘ ์ฃผํŒŒ์ˆ˜ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ์ธก์ •์น˜์˜ ๋ฌด-์ „๋ฆฌ์ธต ์กฐํ•ฉ์„ ํ™œ์šฉํ•˜์—ฌ ์ „๋ฆฌ์ธต ๋ณด์ •์ •๋ณด ์—†์ด๋„ ์ •ํ™•ํ•˜๊ฒŒ ๋ฏธ์ง€์ •์ˆ˜ ๊ฒฐ์ • ๊ฐ€๋Šฅํ•œ ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์œ„์„ฑ Code/Phase ์‹œ๊ณ„ ๋ณด์ •์ •๋ณด๋Š” ํ†ต์‹  ์ง€์—ฐ ๋ฐ ๊ณ ์žฅ ์‹œ ์šฐ์ˆ˜ํ•˜๊ณ  ๊ฐ•๊ฑดํ•œ ์„ฑ๋Šฅ์„ ์œ„ํ•ด ๋‹ค์ค‘ ๊ธฐ์ค€๊ตญ์˜ ๋ชจ๋“  ์ธก์ •์น˜๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ถ”์ •๋œ๋‹ค. ์ด ๋•Œ ๊ฐ ๊ธฐ์ค€๊ตญ ๋ณ„ ์„œ๋กœ ๋‹ค๋ฅธ ๋ฏธ์ง€์ •์ˆ˜ ๋•Œ๋ฌธ์— ๋ฐœ์ƒํ•˜๋Š” ๋ฌธ์ œ๋Š” ์•ž์„œ ์ •ํ™•ํ•˜๊ฒŒ ๊ฒฐ์ •๋œ ๊ธฐ์ค€๊ตญ ๊ฐ„ ์ด์ค‘์ฐจ๋ถ„ ๋œ ๋ฏธ์ง€์ •์ˆ˜๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ˆ˜์ค€์„ ์กฐ์ •ํ•˜๋Š” ๊ณผ์ •์„ ํ†ตํ•ด ํ•ด๊ฒฐ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ƒ์„ฑ๋œ ์œ„์„ฑ Code/Phase ๋ณด์ •์ •๋ณด ๋ฉ”์‹œ์ง€์˜ ํฌ๊ธฐ, ๋ณ€ํ™”์œจ, ์žก์Œ ์ˆ˜์ค€์ด ํฌ๊ฒŒ ๊ฐœ์„ ๋˜์—ˆ๊ณ , ํ†ต์‹  ์ง€์—ฐ ์‹œ ์˜ค์ฐจ ๋ณด์ƒ ์„ฑ๋Šฅ์ด ๊ธฐ์กด RTK ํ”„๋กœํ† ์ฝœ ๋ณด๋‹ค 99% ํ–ฅ์ƒ ๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋Œ€๋ฅ˜์ธต ๋ณด์ •์ •๋ณด๋Š” ์ ์€ ์ˆ˜์˜ ๊ธฐ์ค€๊ตญ ๋งŒ์„ ํ™œ์šฉํ•˜์—ฌ ์ •ํ™•ํ•˜๊ฒŒ ๋Œ€๋ฅ˜์ธต์„ ๋ชจ๋ธ๋งํ•˜๊ธฐ ์œ„ํ•ด ์ž๋™ ๊ธฐ์ƒ๊ด€์ธก์‹œ์Šคํ…œ์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜์ง‘ํ•œ ๊ธฐ์ƒ ์ •๋ณด๋ฅผ ์ถ”๊ฐ€๋กœ ํ™œ์šฉํ•˜์—ฌ ์ƒ์„ฑ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” GNSS ๊ธฐ์ค€๊ตญ ๋„คํŠธ์›Œํฌ๋กœ๋ถ€ํ„ฐ ์ •๋ฐ€ํ•˜๊ฒŒ ์ถ”์ •๋œ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜ ์ˆ˜์ง ๋Œ€๋ฅ˜์ธต ์ง€์—ฐ๊ณผ ๊ธฐ์ƒ์ •๋ณด ๊ธฐ๋ฐ˜์œผ๋กœ ๋ชจ๋ธ๋ง ๋œ ์ˆ˜์ง ๋Œ€๋ฅ˜์ธต ์ง€์—ฐ์„ ํ•จ๊ป˜ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ตฌ๋ฉด์กฐํ™”ํ•จ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ Network RTK ๋ฐ PPP-RTK ๋ณด๋‹ค ํ•„์š”ํ•œ ๋ฉ”์‹œ์ง€ ์–‘๊ณผ ๊ธฐ์ค€๊ตญ ์ˆ˜๋ฅผ ํฌ๊ฒŒ ๊ฐ์†Œ์‹œํ‚ค๋ฉด์„œ๋„ RMS 2 cm ์ˆ˜์ค€์œผ๋กœ ์ •ํ™•ํ•œ ๋ณด์ •์ •๋ณด ์ƒ์„ฑ์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ Compact Wide-Area RTK ์‹œ์Šคํ…œ์˜ ํ•ญ๋ฒ• ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ๋ฏธ๊ตญ ๋™๋ถ€ ์ง€์—ญ 6๊ฐœ ๊ธฐ์ค€๊ตญ์˜ ์‹ค์ธก GPS ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํ…Œ์ŠคํŠธ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ œ์•ˆํ•œ ์‹œ์Šคํ…œ์€ ๋ฏธ์ง€์ •์ˆ˜ ๊ฒฐ์ • ์ดํ›„ ์‚ฌ์šฉ์ž์˜ 95% ์ˆ˜ํ‰ ์œ„์น˜ ์˜ค์ฐจ 1.9 cm, 95% ์ˆ˜์ง ์œ„์น˜ ์˜ค์ฐจ 7.0 cm ๋กœ ์œ„์น˜๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๊ฒฐ์ •ํ•˜์˜€๋‹ค. ์‚ฌ์šฉ์ž ๋ฏธ์ง€์ •์ˆ˜ ๊ฒฐ์ • ์„ฑ๋Šฅ์€ ๋Œ€๋ฅ˜์ธต ์•ˆ์ • ์ƒํƒœ์—์„œ ์•ฝ 2๋ถ„ ๋‚ด๋กœ 100% ์˜ ์„ฑ๊ณต๋ฅ ์„ ๊ฐ€์ง„๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ์‹œ์Šคํ…œ์ด ํ–ฅํ›„ ํ•œ๊ตญํ˜• ์œ„์„ฑํ•ญ๋ฒ• ์‹œ์Šคํ…œ(KPS, Korean Positioning System)์˜ ์ „๊ตญ ๋‹จ์œ„ ์„ผํ‹ฐ๋ฏธํ„ฐ ๊ธ‰ ์„œ๋น„์Šค๋ฅผ ์œ„ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ํ™œ์šฉ๋˜๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค.CHAPTER 1. Introduction 1 1.1 Motivation and Purpose 1 1.2 Former Research 4 1.3 Outline of the Dissertation 7 1.4 Contributions 8 CHAPTER 2. Overview of GNSS Augmentation System 11 2.1 GNSS Measurements 11 2.2 GNSS Error Sources 14 2.2.1 Traditional GNSS Error Sources 14 2.2.2 Special GNSS Error Sources 21 2.2.3 Summary 28 2.3 GNSS Augmentation System 29 2.3.1 Satellite-Based Augmentation System (SBAS) 29 2.3.2 Real-Time Kinematic (RTK) 32 2.3.3 Precise Point Positioning (PPP) 36 2.3.4 Summary 40 CHAPTER 3. Compact Wide-Area RTK System Architecture 43 3.1 Compact Wide-Area RTK Architecture 43 3.1.1 WARTK Reference Station (WRS) 48 3.1.2 WARTK Processing Facility (WPF) 51 3.1.3 WARTK User 58 3.2 Ambiguity Resolution and Validation Algorithms of Compact Wide-Area RTK System 59 3.2.1 Basic Theory of Ambiguity Resolution and Validation 60 3.2.2 A New Ambiguity Resolution Algorithms for Multi-Frequency Signals 65 3.2.3 Extra-Wide-Lane (EWL) Ambiguity Resolution 69 3.2.4 Wide-Lane (WL) Ambiguity Resolution 71 3.2.5 Narrow-Lane (NL) Ambiguity Resolution 78 3.3 Compact Wide-Area RTK Corrections 83 3.3.1 Satellite Orbit Corrections 86 3.3.2 Satellite Code/Phase Clock (CPC) Corrections 88 3.3.3 Tropospheric Corrections 89 3.3.4 Message Design for GEO Broadcasting 90 CHAPTER 4. Code/Phase Clock (CPC) Correction Generation Algorithm 93 4.1 Former Research of RTK Correction Protocol 93 4.1.1 Observation Based RTK Data Protocol 93 4.1.2 Correction Based RTK Data Protocol 95 4.1.3 Compact RTK Protocol 96 4.2 Satellite CPC Correction Generation Algorithm 100 4.2.1 Temporal Decorrelation Error Reduced Methods 102 4.2.2 Ambiguity Level Adjustment 105 4.2.3 Receiver Clock Synchronization 107 4.2.4 Averaging Filter of Satellite CPC Correction 108 4.2.5 Ambiguity Re-Initialization and Message Generation 109 4.3 Correction Performance Analysis Results 111 4.3.1 Feasibility Test Environments 111 4.3.2 Comparison of RTK Correction Protocol 113 4.3.3 Latency Compensation Performance Analysis 116 4.3.4 Message Data Bandwidth Analysis 119 CHAPTER 5. Tropospheric Correction Generation Algorithm 123 5.1 Former Research of Tropospheric Correction 123 5.1.1 Tropospheric Corrections for SBAS 124 5.1.2 Tropospheric Corrections of Network RTK 126 5.1.3 Tropospheric Corrections of PPP-RTK 130 5.2 Tropospheric Correction Generation Algorithm 136 5.2.1 ZWD Estimation Using Carrier-Phase Observations 138 5.2.2 ZWD Measurements Using Weather Data 142 5.2.3 Correction Generation Using Spherical Harmonics 149 5.2.4 Correction Applying Method for User 157 5.3 Correction Performance Analysis Results 159 5.3.1 Feasibility Test Environments 159 5.3.2 Zenith Correction Domain Analysis 161 5.3.3 Message Data Bandwidth Analysis 168 CHAPTER 6. Compact Wide-Area RTK User Test Results 169 6.1 Compact Wide-Area RTK User Process 169 6.2 User Performance Test Results 173 6.2.1 Feasibility Test Environments 173 6.2.2 User Range Domain Analysis 176 6.2.3 User Ambiguity Domain Analysis 182 6.2.4 User Position Domain Analysis 184 CHAPTER 7. Conclusions 189 Bibliography 193 ์ดˆ ๋ก 207Docto

    Reduction of initial convergence period in GPS PPP data processing

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    Precise Point Positioning (PPP) has become a popular technique to process data from GPS receivers by applying precise satellite orbit and clock information, along with other minor corrections to produce cm to dm-level positioning. Although PPP presents definite advantages such as operational flexibility and cost effectiveness for users, it requires 15-25 minutes initialization period as carrier-phase ambiguities converge to constant values and the solution reaches its optimal precision. Pseudorange multipath and noise are the largest remaining unmanaged errors source in PPP. It is proposed that by reducing these effects carrier-phase ambiguities will reach the correct steady state at an earlier time, thus reducing the convergence period of PPP. Given this problem, this study seeks to improve management of these pseudorange errors. The well-known multipath linear combination was used in two distinct ways: 1) to directly correct the raw pseudorange observables, and 2) to stochastically de-weight the pseudorange observables. Corrections to the observables were made in real-time using data from the day before, and post-processed using data from the same day. Post-processing has shown 4 7% improvement in the rate of convergence, as the pseudorange multipath and noise were effectively mitigated. A 36% improvement in the rate of convergence was noted when the pseudorange measurements were stochastically de-weighting using the multipath observable. The strength of this model is that it allows for real-time compensation of the effects of the pseudorange multipath and noise in the stochastic model

    Identification of multi-faults in GNSS signals using RSIVIA under dual constellation

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    This publication presents the development of integrity monitoring and fault detection and exclusion (FDE) of pseudorange measurements, which are used to aid a tightly-coupled navigation filter. This filter is based on an inertial measurement unit (IMU) and is aided by signals of the global navigation satellite system (GNSS). Particularly, the GNSS signals include global positioning system (GPS) and Galileo. By using GNSS signals, navigation systems suffer from signal interferences resulting in large pseudorange errors. Further, a higher number of satellites with dual-constellation increases the possibility that satellite observations contain multiple faults. In order to ensure integrity and accuracy of the filter solution, it is crucial to provide sufficient fault-free GNSS measurements for the navigation filter. For this purpose, a new hybrid strategy is applied, combining conventional receiver autonomous integrity monitoring (RAIM) and innovative robust set inversion via interval analysis (RSIVIA). To further improve the performance, as well as the computational efficiency of the algorithm, the estimated velocity and its variance from the navigation filter is used to reduce the size of the RSIVIA initial box. The designed approach is evaluated with recorded data from an extensive real-world measurement campaign, which has been carried out in GATE Berchtesgaden, Germany. In GATE, up to six Galileo satellites in orbit can be simulated. Further, the signals of simulated Galileo satellites can be manipulated to provide faulty GNSS measurements, such that the fault detection and identification (FDI) capability can be validated. The results show that the designed approach is able to identify the generated faulty GNSS observables correctly and improve the accuracy of the navigation solution. Compared with traditional RSIVIA, the designed new approach provides a more timely fault identification and is computationally more efficient
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