• 제어로봇시스템학회논문지
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• 제20권5호
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• pp.584-591
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• 2014

This paper presents a novel aided navigation method for AUV (Autonomous Underwater Vehicles). The navigation system for AUV includes several sensors such as IMU (Inertial Measurement Unit), DVL (Doppler Velocity Log) and depth sensor. In general, the $13^{th}$ order INS error model, which includes depth error, velocity error, attitude error, and the accelerometer and gyroscope biases as state variables is used with measurements from DVL and depth sensors. However, the model may degrade the estimation performance of the heading state. Therefore, the $11^{th}$ INS error model is proposed. Its validity is verified by using a degree of observability and analyzing steady state error. The performance of the proposed model is shown by the computer simulation. The results show that the performance of the reduced $11^{th}$ order error model is better than that of the conventional $13^{th}$ order error model.

• 한국항공우주학회지
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• 제47권6호
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• pp.438-445
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• 2019

본 논문에서는 MEMS 자이로스코프에서 발생하는 G-민감도 오차를 관성센서 오차 모델에 정의하고, 이를 분석하여 오차 성분을 추출하는 기법을 제안한다. 일반적으로 MEMS기반 자이로스코프는 스프링과 관성질량체를 갖는 진동형 방식으로 개발된다. 따라서 구조적으로 고기동 환경에서 인가되는 가속도에 비례하는 G-민감도 오차 특성을 갖게 된다. 이러한 G-민감도 오차는 외부에서 높은 가속도가 인가되지 않는 민수분야에서는 무시할 정도로 작다. 하지만 전술급 성능의 MEMS 관성측정기가 고가속 환경에서 외란과 가속도에 의해 G-민감도 오차가 발생하게 되면 항체의 유도조종을 위한 항법장치 성능에 큰 영향을 미치게 되므로 오차 분석과 보상은 필수적이다. 따라서 본 논문에서는 MEMS 자이로스코프에 발생하는 G-민감도 오차를 분석하고 정의하여 관성센서 오차모델에 적용한다. 새로 정의된 관성센서 오차모델을 분석하여, 오차 성분을 고가속도 시험환경이 아닌 FMS 시험만으로 정확히 추출하는 방법을 제안한다. 그리고 제안한 방법으로 얻은 오차를 보상하여 고가속도 시험을 수행하고 그 결과를 분석하여 성능과 신뢰성을 검증한다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.109-113
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• 2006

This paper presents an in-flight alignment method for strapdown inertial navigation systems based on the line-of-sight information. Unlike the existing methods, the proposed method utilizes only the 2-axis angle measurements of the onboard image sensor and does not require any explicit range measurements between the vehicle and landmarks. To improve the accuracy of all the position, velocity, and attitude estimates through the in-flight alignment, an error model of the image-sensor-aided SDINS is derived. A simulation study demonstrates that the accuracy of SDINS can be improved by the line-of-sight information only.

• 제어로봇시스템학회논문지
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• 제7권11호
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• pp.944-952
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• 2001

Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it is desired to combine INS with external aids such as GPS. However GPS informations have a randomly abrupt jump due to a sudden corruption of the received satellite signals and environment, and moreover GPS can\`t provide navigation solutions. In this paper, smoothing and prediction schemes are proposed for GPS`s jump or unavailable GPS. The smoothing algorithm which is designed as a scalar adaptive filter, smooths abrupt jump. The prediction algorithm which is proved by Schuler error model of INS, estimates INS error in appropriate time. The outputs of proposed algorithm apply stable measurements to GPS aided INS Kalman filter. Simulations show that the proposed algorithm can effectively remove measurement jump and predict INS error.

• 제어로봇시스템학회논문지
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• 제12권5호
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• pp.509-515
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• 2006

This paper presents a navigation error compensation scheme for Strap-Down Inertial Navigation System(SDINS) using electro-optical sensor. The proposed scheme uses the position or the attitude information from the sensor. For each case, Kalman filter model is derived and implemented. To show the effectiveness of the present compensation scheme, computer simulations have been carried out resulting in the boundedness of position and attitude errors.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.571-574
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• 1997

항법장치의 핵심요소인 가속도센서와 자이로센서는 선형거리추측(Linear position estimation)과 각 변위 추측(orientation estimation)시 출력 데이터에 포함된 오차성분의 적분에 의하여 시간이 증가함에 따라 선형거리 오차와 각 변위 오차가 누적된다. 이에 따라 본 논문에서는 정밀한 항법을 위한 저가의 IMU (Inertial Measurement Unit)를 설계하고, 오차성분의 사전해석을 통하여 정확한 오차모델을 찾는데 그 목적이 있다.

• 전기학회논문지
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• 제58권5호
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• pp.1010-1018
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• 2009

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.

• Journal of Positioning, Navigation, and Timing
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• 제7권3호
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• pp.165-173
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• 2018

Underwater vehicles use Inertial Navigation System (INS) with high-performance Inertial Measurement Unit (IMU) for high precision navigation. However, when underwater navigation is performed for a long time, the INS error gradually diverges, therefore, an integrated navigation method using auxiliary sensors is used to solve this problem. In terms of underwater vehicles, the vertical axis error is primarily compensated through Vertical Channel Damping (VCD) using a depth gauge, and an integrated navigation filter can be designed to perform horizontal axis error and sensor error correction using a speedometer such as Electromagnetic-Log (EM-Log). However, since EM-Log outputs the forward direction relative speed of the vehicle with respect to the sea and sea current, INS correction filter using this may cause a rather large error. Although it is possible to design proper filters if the exact model of the sea current is known, it is impossible to know the accurate model in reality. Therefore, this study proposes an INS/EM-Log integrated navigation filter with the function to estimate sea current using an Interacting Multiple Model (IMM) filters, and the performance of this filter is analyzed through a simulation performed in various environments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1679-1683
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• 1997

In this paper a linear Kalman filter for calibration of gimballed inertial navigation system(GINS) is designed and its performace is analyzed through the simulation with a real navigation data. Simulation results show that the proposed Kalman filter gives a good performance to calibrate the sensor errors.

• 한국해양공학회지
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• 제19권6호통권67호
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• pp.78-85
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• 2005

Initial alignment and localization are important topics in inertial navigation systems, since misalignment and initial position error wholly propagate into the navigation systems and deteriorate the performance of the systems. This paper presents the error convergence characteristics of the hybrid navigation system for underwater vehicles initial position, which is based on an inertial measurement unit (IMU) accompanying a range sensor. This paper demonstrates the improvement on the navigational performance oj the hybrid system with the range information, especially focused on the convergence of the estimation of underwater vehicles initial position error. Simulations are performed with experimental data obtained from a rotating ann test with a fish model. The convergence speed and condition of the initial error removal for random initial position errors are examined with Monte Carlo simulation. In addition, numerical simulation is conducted with an AUV model in lawn-mowing survey mode to illustrate the error convergence of the hybrid navigation System for initial position error.