• 한국항행학회논문지
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• 제24권3호
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• pp.198-204
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• 2020

본 논문에서는 GNSS (global navigation satellite system)이 가용하지 않는 상황에서 시간이 지남에 따라 오차가 누적되는 특성을 가진 관성항법장치(inertial navigation system)의 항법 오차를 보상하기 위한 EM-Log (electromagnetic-log) 보정항법 필터를 설계하였다. EM-Log는 해상에서 운동체의 이동 속도를 측정하여 속도 오차를 보정하여 주나 측정된 속도에는 해조류가 포함되어 있기 때문에 적절한 해조류 모델 설계와 추정이 필요하다. 본 논문에서는 해조류 추정을 위해 단일 모델 필터와 IMM (interacting multiple model) 모델 필터 방법론을 제시하고 설계된 필터의 해조류 추정 성능을 확인한 후 해조류 모델 설계가 필터 성능에 어떤 영향을 주는지 분석하였다. 설계된 보정항법 필터의 성능은 시뮬레이션을 이용하여 검증하고 순수항법 대비 필터 성능 향상률을 비교 분석하였다. 단일 모델 필터는 해조류 모델이 동일한 경우 성능이 좋지만 해조류 모델이 동일하지 않을 경우 성능이 저하되는 것을 확인 할 수 있었다. 반면, IMM 모델 필터의 경우 다양한 해조류 모델을 사용하기 때문에 단일 모델필터 대비 안정적인 성능을 유지하는 것을 확인하였다.

• Journal of Positioning, Navigation, and Timing
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• 제4권4호
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• pp.181-186
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• 2015

A High-degree Cubature Kalman Filter (CKF) is proposed to deal with the Strapdown Inertial Navigation System (SDINS) alignment problem. In-flight Alignment (IFA) is an effective method to compensate for attitude errors of the navigation system. While providing precise attitude error compensation, however, the external source aided alignment often creates a nonlinear filtering problem caused by a large misalignment angle. Introduced recently, Cubature Kalman Filter is a suitable technique for various nonlinear problems. In this paper, a higher degree CKF is applied to this accuracy-is-everything SDINS IFA problem. The simulation results show that the proposed technique outperformed a traditional nonlinear filter in terms of precision and alignment time.

• 제어로봇시스템학회논문지
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• 제9권6호
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• pp.420-424
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• 2003

A new adaptive filter is proposed for an aided strapdown inertial navigation system(SDINS). The proposed filter can be used to effectively estimate the time-varying variance of the measurement noise. Then, the in-flight alignment for the radar aided SDINS is designed using the additive quatermion error model. Simulation results show that the proposed adaptive filter effectively improves the performance of the radar aided SDINS.

• 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.

• Advances in aircraft and spacecraft science
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• 제7권1호
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• pp.1-17
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• 2020

In this paper, a new model-based Fault Detection and Diagnosis (FDD) method for an agile supersonic flight vehicle is presented. A nonlinear model, controlled by a classical closed loop controller and proportional navigation guidance in interception scenario, describes the behavior of the vehicle. The proposed FDD method employs the Inertial Navigation System (INS) data and nonlinear dynamic model of the vehicle to inform fins damage to the controller before leading to an undesired performance or mission failure. Broken, burnt, unactuated or not opened control surfaces cause a drastic change in aerodynamic coefficients and consequently in the dynamic model. Therefore, in addition to the changes in the control forces and moments, system dynamics will change too, leading to the failure detection process being encountered with difficulty. To this purpose, an equivalent aerodynamic model is proposed to express the dynamics of the vehicle, and the health of each fin is monitored by the value of a parameter which is estimated using an adaptive robust filter. The proposed method detects and isolates fins damages in a few seconds with good accuracy.

• 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.

• 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.

• 한국항공우주학회지
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• 제34권4호
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• pp.47-52
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• 2006

불규칙 혼합잡음의 등가 ARMA 모델 표현을 사용하여 자이로의 불규칙 혼합잡음을 고려하는 보조항법시스템 칼만필터 설계 방법을 제안한다. 필터 설계 절차는 먼저 보조항법 시스템에 사용되는 필터는 간접 되먹임 칼만필터임을 고려하여 등가 ARMA 모델로 표현된 자이로 불규칙 잡음의 시간 차분을 구한다. 다음으로 시간 차분된 ARMA 모델을 상태 방정식으로 표현하는데 AR과 MA 차수에 따라 두 가지로 나누어진다. 먼저 AR 차수가 큰 경우 가제어 혹은 가관측 특이형태를 사용한다. MA 차수가 큰 경우에는 몇 단계 이후의 예측치를 상태변수로 하는 상태방정식을 사용하는데, 이때 자이로 출력을 보상하는 값에 따라 다시 고차수 필터와 저차수 필터로 구분된다. 마지막으로 자이로 불규칙 잡음을 보조항법시스템 칼만필터에 포함시켜 최종적인 필터 모델을 얻는다. 시뮬레이션 결과를 통하여 제안된 고차수 및 저차수 필터 모두 혼합잡음을 백색잡음으로 간주한 기존의 필터보다 항법오차를 감소시킬 수 있음을 보임으로써 그 효용성을 제시한다.

• International Journal of Aeronautical and Space Sciences
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• 제14권4호
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• pp.369-378
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• 2013

This paper presents a vision/LiDAR integrated navigation system that provides accurate relative navigation performance on a general ground surface, in GNSS-denied environments. The considered ground surface during flight is approximated as a piecewise continuous model, with flat and slope surface profiles. In its implementation, the presented system consists of a strapdown IMU, and an aided sensor block, consisting of a vision sensor and a LiDAR on a stabilized gimbal platform. Thus, two-dimensional optical flow vectors from the vision sensor, and range information from LiDAR to ground are used to overcome the performance limit of the tactical grade inertial navigation solution without GNSS signal. In filter realization, the INS error model is employed, with measurement vectors containing two-dimensional velocity errors, and one differenced altitude in the navigation frame. In computing the altitude difference, the ground slope angle is estimated in a novel way, through two bisectional LiDAR signals, with a practical assumption representing a general ground profile. Finally, the overall integrated system is implemented, based on the extended Kalman filter framework, and the performance is demonstrated through a simulation study, with an aircraft flight trajectory scenario.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.239-246
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• 2004

In this paper, we develop and implement a high integrity GNC(Guidance, Navigation, and Control) system, based on the combined use of the Global Positioning System (GPS) and an Inertial Measurement Unit (IMU), for autonomous land vehicle applications. This paper highlights guidance for the predetermined trajectory and navigation with detection of possible faults during the fusion process in order to enhance the integrity of the navigation loop. The implementation of the GNC system to the autonomous land vehicle presented with fault detection methodology considers high frequency faults from the GPS receiver caused by shadowing and multipath error The implementation, based on a low-cost, strapdown INS aided by standard GPS technology, is described. The results of the field test in the urban environment are presented and showed effectiveness of the GNC system.