10 research outputs found

    ๊ฐ•์ธํ•„ํ„ฐ ์„ค๊ณ„ ๋ฐ ์ŠคํŠธ๋žฉ๋‹ค์šด ๊ด€์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ์—์„œ์˜ ์‘์šฉ

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2002.Docto

    Flexure Error Analysis of RLG based INS

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    Any input acceleration that bends RLG dithering axis causes flexure error, which is a source of the noncommutative error that can not be compensated by simply using integrated gyro sensor output. This paper introduces noncommutative error equations that define attitude errors caused by flexure errors. In this paper, flexure error is classified as sensor level error if the sensing axis coincides with the dithering axis and as system level error if the two axes do not coincide. The relationship between gyro output and the rotation vector is introduced and is used to define the coordinate transformation matrix and angular motion. Equations are derived for both sensor level and system level flexure error analysis. These equations show that RLG based INS attitude error caused by flexure is directly proportional to time, amount of input acceleration and the dynamic frequency of the vehicle.๋ณธ ๋…ผ๋ฌธ์€ ๋‘์‚ฐ์ธํ”„๋ผ์ฝ”์–ด(์ฃผ)์™€ ๊ตญ๋ฐฉ๊ณผํ•™์—ฐ๊ตฌ์†Œ์˜ ๋ถ€๋ถ„์  ์ง€์›์— ์˜ํ•˜์—ฌ ์—ฐ๊ตฌ๋˜์—ˆ์Œ

    Noncommutativity Error Analysis with RLG-based INS

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    In this paper, we analyze a noncommutativity error that is not able to be compensated with integrating gyro outputs in RLG-based INS. The system can suffer from some motion known as RLG dithering motion, coning motion, ISA motion derived by an AV mount and vehicle real dynamic motion. So these motions are a cause of the noncornmutativity error, the system error derived by each motion has to be analyzed. For the analysis, a relation between rotation vector and gyro outputs is introduced and applied to define the coordinate transformation matrix and the angular vector

    A Performance Comparison of Extended and Unscented Kalman Filters for INS/GPS Tightly Coupled Approach

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    This paper deals with INS/GPS tightly coupled integration algorithms using extend Kalman filter (EKF) and unscented Kalman filter (UKF). In the tightly coupled approach, nonlinear pseudorange measurement models are used for the INS/GPS integration Kalman filter. Usually, an EKF is applied for this task, but it may diverge due to poor functional linearization of the nonlinear measurement. The UKF approximates a distribution about the mean using a set of calculated sigma points and achieves an accurate approximation to at least second-order. We introduce the generalized scaled unscented transformation which modifies the sigma points themselves rather than the nonlinear transformation. The generalized scaled method is used to transform the pseudo range measurement of the tightly coupled approach. To compare the performance of the EKF- and UKF-based tightly coupled approach, real van test and simulation have been carried out with feedforward and feedback indirect Kalman filter forms. The results show that the UKF and EKF have an identical performance in case of the feedback filter form, but the superiority of the UKF is demonstrated in case ofthe feedforward filer form.๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตญ๋ฐฉ๊ณผํ•™์—ฐ๊ตฌ์†Œ์˜ ๋ถ€๋ถ„์  ์ง€์›์— ์˜ํ•˜์—ฌ ์—ฐ๊ตฌ๋˜์—ˆ์Œ

    Hybrid Dual Quaternion Algorithm For Precise Strapdown Inertial Navigation

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    Dual quaternion is efficient methodology to express rotation and translation of the vehicles movements in the unified frame work. Recently, a strapdown inertial navigation algorithm based on dual quaternion was introduced. By comparing and analyzing the classical and dual-quaternion algorithms, this paper proposes a new strapdown inertial navigation algorithm that maintains the accurracy benefit of the dual-quaternion algorithm with considerable computational reduction. Simulation results show the efficiency of the proposed hybrid strapdown navigation algorithm. ๊ทผ๋ž˜์— ๋“ค์–ด ๊ด€์„ฑํ•ญ๋ฒ• ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ์— ์ด์ฒด ์ฟผํ„ฐ๋‹ˆ์–ธ(dual quaternion)์ด ์ƒˆ๋กœ์ด ์ ์šฉ๋˜๊ธฐ ์‹œ์ž‘ํ•˜์˜€๋‹ค. ์ด์ฒด ์ฟผํ„ฐ๋‹ˆ์–ธ์€ ์„ ํ˜• ๋ฏธ๋ถ„๋ฐฉ์ •์‹์œผ๋กœ ๊ณ„์‚ฐํ•˜๋Š” ์ฟผํ„ฐ๋‹ˆ์–ธ์„ ํšŒ์ „์šด๋™๊ณผ ๋ณ‘์ง„์šด๋™์„ ๋™์‹œ์— ์ทจ๊ธ‰ํ•˜๋Š” ์ด์ฒด์ˆ˜(dual number)์ฒด๊ณ„๋กœ ํ™•์žฅํ•œ ํ˜•ํƒœ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด์˜ ๊ด€์„ฑํ•ญ๋ฒ• ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ทผ๋ž˜์— ์†Œ๊ฐœ๋œ ์ด์ฒด ์ฟผํ„ฐ๋‹ˆ์–ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๋ถ„์„ํ•˜์—ฌ ๋‘ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์žฅ์ ์„ ๊ฒฐํ•ฉํ•œ ์ƒˆ๋กœ์šด ํ˜•ํƒœ์˜ ์ •๋ฐ€ ์ด์ฒด ์ฟผํ„ฐ๋‹ˆ์–ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ํ˜ผํ•ฉ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ธฐ์กด ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์œ ์‚ฌํ•œ ์ˆ˜์น˜์  ์—ฐ์‚ฐ๋Ÿ‰์œผ๋กœ ์ด์ฒด ์ฟผํ„ฐ๋‹ˆ์–ธ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์œ ์‚ฌํ•œ ์ •ํ™•๋„ ํ–ฅ์ƒ์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ์‹œ์œจ ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ์ œ์•ˆ๋œ ํ˜ผํ•ฉ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์—ฐ์‚ฐ๋Ÿ‰ ๋ฐ ์ •ํ™•๋„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค.ADD/FVRC/ํ•œ๊ตญํ•ญ๊ณต๋Œ€ํ•™๊ต ํ•ญ๊ณต์ „์ž์—ฐ๊ตฌ

    The design of the GPS Signal Acquisition Algorithm for Weak Signals

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    ์†Œํ”„ํŠธ์›จ์–ด ๊ธฐ๋ฐ˜ GPS์ˆ˜์‹ ๊ธฐ์—์„œ ์‹ ํ˜ธํš๋“ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ค‘์š”ํ•œ ์‹ ํ˜ธ์ฒ˜๋ฆฌ ๊ณผ์ • ์ค‘ ํ•˜๋‚˜๋‹ค. ์‹ ํ˜ธํš๋“ ๊ณผ์ •์—์„œ ๊ตฌํ•œ ์ฝ”๋“œ์œ„์ƒ๊ณผ ๋„ํ”Œ๋Ÿฌ ์ด๋™๋œ ์ฃผํŒŒ์ˆ˜๋ฅผ ๋‹ค์Œ๋‹จ๊ณ„์ธ ์‹ ํ˜ธ ์ถ”์ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋ณด๋‚ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ •ํ™•ํ•˜๊ณ  ํšจ์œจ์ ์ธ ์‹ ํ˜ธ ํš๋“ ํŒ๋ณ„ ๋ฐฉ๋ฒ•์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ ํ˜ธ ํš๋“ ํŒ๋ณ„ ๋ฐฉ๋ฒ• ์ค‘ ์ˆœํ™˜์ƒ๊ด€๊ด€๊ณ„ ๊ฐ’์˜ ๋น„๋ฅผ ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ ์„ค์ •ํ•˜์—ฌ ์‹ ํ˜ธ ํš๋“ ์—ฌ๋ถ€๋ฅผ ํŒ๋ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ–ˆ๋‹ค. ์„ค์ •ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์˜ค๊ฒฝ๋ณด์œจ๊ณผ ๊ฒ€์ถœํ™•๋ฅ ์„ ํ†ตํ•ด ์„ฑ๋Šฅ์„ ๋ถ„์„ํ–ˆ๊ณ , ๋ฏธ์•ฝ ์‹ ํ˜ธ ํš๋“์„ ์œ„ํ•œ ์ž„๊ณ„์น˜ ์„ค์ • ๋ฐฉ๋ฒ•์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ถ„์„ํ•œ ์ž„๊ณ„์น˜๋ฅผ ๊ฐ€์ง€๊ณ  ์‹ค์ œ GPS์‹ ํ˜ธ๋ฅผ ํš๋“ํ•˜๋Š”๋ฐ ์‚ฌ์šฉํ•˜์˜€๋‹ค.๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฐฉ์œ„์‚ฌ์—…์ฒญ๊ณผ ๊ตญ๋ฐฉ๊ณผํ•™์—ฐ๊ตฌ์†Œ๊ฐ€ ์ง€์›ํ•˜๋Š” ๊ตญ๋ฐฉ์œ„์„ฑํ•ญ๋ฒ•ํŠนํ™”์—ฐ๊ตฌ์„ผํ„ฐ ์‚ฌ์—…์˜ ์ผํ™˜์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ์Šต๋‹ˆ๋‹ค

    GNSS Interference Detection using Adaptive notch filter

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    ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ์˜ ํ™œ์šฉ์ด ์ฆ๊ฐ€ํ•˜๋ฉด์„œ ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ์ €ํ•˜์‹œํ‚ค๋Š” ์›์ธ ์ค‘ ํ•˜๋‚˜์ธ ์ „ํŒŒ๊ฐ„์„ญ ์‹  ํ˜ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ๋†’์•„์ง€๊ณ  ์žˆ๋‹ค. ์ „ํŒŒ๊ฐ„์„ญ ์‹ ํ˜ธ์˜ ์˜ํ–ฅ์— ๊ฐ•์ธํ•˜๊ณ  ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ์˜ ํ•ญ๋ฒ•ํ•ด ์ •ํ™•๋„๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ „ํŒŒ๊ฐ„์„ญ ์‹ ํ˜ธ์›์„ ๊ฒ€์ถœํ•˜๊ณ  ์ „ํŒŒ๊ฐ„์„ญ ์˜ํ–ฅ์„ ์™„ํ™”์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ ์‘ ๋…ธ์น˜ ํ•„ํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ˜‘๋Œ€์—ญ ๋‹จ์ผ์ฃผํŒŒ์ˆ˜ ์ „ํŒŒ๊ฐ„์„ญ ์‹ ํ˜ธ (single-tone CWI)์™€ ๋‹ค์ค‘์ฃผํŒŒ์ˆ˜ ์ „ํŒŒ๊ฐ„์„ญ ์‹ ํ˜ธ(multi-tone CWI)๋ฅผ ๊ฒ€์ถœํ•˜๊ธฐ ์œ„ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌํ˜„ํ•˜ ๊ณ  ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ๊ฒ€์ถœํ•˜๊ณ  ๊ฒฐ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. In this paper, an adaptive IIR notch filter structure based on a low-complexity time domain approach and the interference detection algorithms is employed to track single-tone CW and multi-tone CW interference signal. The center frequency of the interference is detected using the adaptive lattice IIR notch filter. Theoretical analysis and simulation are carried out to show the feasibility and performance estimation of the proposed methods.DAPA/ADD/NGRC(๊ตญ๋ฐฉ์œ„์„ฑํ•ญ๋ฒ•ํŠนํ™”์—ฐ๊ตฌ์„ผํ„ฐ
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