• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.80-80
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• 2000

The GPS can provide accurate position information on the earth. But GPS receiver can't give position information inside buildings. DR(Dead-Reckoning) or INS(Inertial Navigation System) gives position information continuously indoors as well as outdoors, because they do not depend on the external navigation information. But in general, the inertial sensors severely suffer from their drift errors, the error of these navigation system increases with time. GPS and DR sensors can be integrated together with Kalman filter to overcome these problems. In this paper, we developed a personal navigation system which can be carried by person, using GPS and electronic pedometer. The person's footstep is detected by an accelerometer installed in vertical direction and the direction of movement is sensed by gyroscope and magnetic compass. In this case the step size is varying with person and changing with circumstance, so determining step size is the problem. In order to calculate the step size of detected footstep, the neural network method is used. The teaming pattern of the neural network is determined by human walking pattern data provided by 3-axis accelerometer and gyroscope. We can calculate person's location with displacement and heading from this information. And this neural network method that calculates step size gives more improved position information better than fixed step size.

• 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.

• 로봇학회논문지
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• 제15권1호
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• pp.16-23
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• 2020

This paper describes an alignment algorithm that estimates the initial heading angle of AUVs (Autonomous Underwater Vehicle) for starting navigation in a sea area. In the basic dead reckoning system, the initial orientation of the vehicle is very important. In particular, the initial heading value is an essential factor in determining the performance of the entire navigation system. However, the heading angle of AUVs cannot be measured accurately because the DCS (Digital Compass) corrupted by surrounding magnetic field in pointing true north direction of the absolute global coordinate system (not the same to magnetic north direction). Therefore, we constructed an experimental constraint and designed an algorithm based on extended Kalman filter using only inertial navigation sensors and a GPS (Global Positioning System) receiver basically. The value of sensor covariance was selected by comparing the navigation results with the reference data. The proposed filter estimates the initial heading angle of AUVs for navigation in a sea area and reflects sampling characteristics of each sensor. Finally, we verify the performance of the filter through experiments.

• 제어로봇시스템학회논문지
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• 제7권5호
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• pp.454-464
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• 2001

In this paper, a personal navigation system is developed using GPS and dead reckoning sensors. This personal navigation system can be used to track a person inside a building, on an urban street, and in the mountain area. GPS can provide accurate absolute position information, but it cant be used without receiving enough satellite signals. Although the inertial sensors such as gyro an accelerometer and be used without this diggiculty, the inertial sensors severely suffer from their drift errors and the magne-tometer can be easily distorted by surrounding electromagnetic field. GPS and DR sensors can be inte-grated together to overcome these problems. A new personal navigation system that can be carried wit person is developed. A pedometer. actually vertically mounted accelerometer, detects ones footstep and gyro detects heading angle. These DR sensors are integrated with GPS and the humans walking pattern provides additional navigation information for compensating the DR sensors. The field testes are performed to evaluated the proposed navigation algorithm.

• Journal of Positioning, Navigation, and Timing
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• 제6권2호
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• pp.59-70
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• 2017

Since GPS signals are vulnerable to interference and obstruction, many alternate aiding systems have been proposed to integrate with an inertial navigation system. Among these alternate systems, the vision-aided method has become more attractive due to its benefits in weight, cost and power consumption. This paper proposes a loosely-coupled vision/INS integrated navigation method which can work in GPS-denied environments. The proposed method improves the navigation accuracy by correcting INS navigation and sensor errors using position and attitude outputs of a landmark based vision navigation system. Furthermore, it has advantage to provide redundant navigation output regardless of INS output. Computer simulations and the van tests have been carried out in order to show validity of the proposed method. The results show that the proposed method works well and gives reliable navigation outputs with better performance.

• 항공우주시스템공학회지
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• 제4권3호
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• pp.17-23
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• 2010

GPS sensors provide accurate position and velocity of moving vehicles. However, GPS is weak at intermittent signal loss and large position error. Combination with INS improves the GPS position accuracy during the GPS signal loss. In this paper, a fusion filter using GPS and INS is developed and its perfomance is analyzed with RC car and RC airplane experiments.

• 전자공학회논문지
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• 제52권5호
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• pp.190-196
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• 2015

본 논문은 항법장치 위치보정을 위해 칼만필터를 적용한 GPS/INS융합의 다중보정방법을 제안한다. 연구에서는 관성항법장치를 구현하기 위해 9축 항법장치로 보정알고리즘을 적용하여 위치오차를 감소시킨 방법을 적용했다. 일반적으로 GPS/INS는 위치정보를 얻어낼 수 있지만 위치정보를 구하는 과정에서 오차 또한 더불어 커지게 되기에 이를 보정하기 위한 강인한 오차 보정 알고리즘이 필요하다. 본 논문에서는 9축 관성센서(mpu-9150)의 외란에 대한 강인성 향상을 위해 가속도계 보정 알고리즘을 사용하여 tilt보정을 수행했으며, 제어 대상체의 정확한 방위를 파악할 수 있도록 Yaw각 재정의 알고리즘을 적용하였다. 최종적으로 GPS/INS와 칼만 필터를 함께 결합한 통합시스템을 구현하였다.

• International Journal of Aeronautical and Space Sciences
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• 제18권3호
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• pp.565-573
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• 2017

This paper presents a multistage in-flight alignment (MIFA) method for a strapdown inertial navigation system (SDINS) suitable for moving vehicles with no initial attitude references. A SDINS mounted on a moving vehicle frequently loses attitude information for many reasons, and it makes solving navigation equations impossible because the true motion is coupled with an undefined vehicle attitude. To determine the attitude in such a situation, MIFA consists of three stages: a coarse horizontal attitude, coarse heading, and fine attitude with adaptive Kalman navigation filter (AKNF) in order. In the coarse horizontal alignment, the pitch and roll are coarsely estimated from the second order damping loop with an input of acceleration differences between the SDINS and GPS. To enhance estimation accuracy, the acceleration is smoothed by a scalar filter to reflect the true dynamics of a vehicle, and the effects of the scalar filter gains are analyzed. Then the coarse heading is determined from the GPS tracking angle and yaw increment of the SDINS. The attitude from these two stages is fed back to the initial values of the AKNF. To reduce the estimated bias errors of inertial sensors, special emphasis is given to the timing synchronization effects for the measurement of AKNF. With various real flight tests using an UH60 helicopter, it is proved that MIFA provides a dramatic position error improvement compared to the conventional gyro compass alignment.

• 항공우주기술
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• 제1권1호
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• pp.28-41
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• 2002

본 논문의 목적은 실제 관성항법센서를 사용하여 개발된 스트랩다운형 관성항법시스템 및 이용 알고리즘에 대한 성능 및 오차를 평가하는 것이다. 시험은 관성항법센서의 조합을 두가지로 나누어 수행하였는데, 서로 다른 바이어스를 갖는 중급의 가속도계와 저급의 가 속도계가 사용되었으며, 자이로의 경우는 FOG(Fiber Optic Gyro)를 사용하였다(SDINS-1, SDINS-2). 관성항법시스템의 성능을 평가하기 위해서 두가지의 시험이 수행되었으며, 3축 운동 시험대를 이용한 지상정지시험과 차량을 이용한 단거리 주행시험을 수행하였다. 단거 리 주행시험의 결과는 정확도 20 mm를 갖는 DGPS(Differential GPS)의 시험결과와 비교 하였으며, 결과 및 오차를 나타내었다.

• 한국항행학회논문지
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• 제25권1호
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• pp.68-77
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• 2021

본 논문에서는 높은 동특성 환경에서 동작이 가능한 GPS/MEMS IMU 통합항법 수신모듈을 설계 및 제작하고, 그 결과를 확인하였다. 설계한 모듈은 RF 수신부, 관성측정부, 신호처리부, 상관기, 항법 S/W로 구성된다. RF 수신부는 저잡음증폭, 주파수 변환, 필터링, 자동이득조절 기능을 수행하고, 관성측정부는 3축 자이로스코프, 가속도계, 지자기센서가 적용된 MEMS급 IMU로부터 측정 데이터를 수집하여 항법S/W로 전달하는 인터페이스를 제공한다. 신호처리부 및 상관기는 FPGA 로직으로 구현하여 필터링 및 상관 값 계산을 수행하고, FPGA 내부 CPU를 사용하여 위성항법, 통합항법 S/W를 구현하였다. 제작된 모듈의 크기는 95.0 × 85.0 × 12.5 mm 이고, 무게는 110g을 확인하였으며, 동적성능 1200m/s, 가속도 10g의 환경에서 규격 이내의 항법정확도 성능을 확인하였다.