• 한국군사과학기술학회지
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• 제12권1호
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• pp.114-122
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• 2009

An adaptive Kalman filter is designed as a post-navigation filter to improve the accuracy of GPS receiver's navigation performance in high dynamic environments. Not only the adaptive Kalman filter reduces the large noise error of navigation data which is obtained by least square method, but also the filter is not degraded as normal Kalman filter in high acceleration movements because the system noise is estimated. Also an initialization structure of the filter is desisted in consideration for irregular output condition of navigation data by least squared method such as reacquisition status in GPS receiver. The filter performance is verified by GPS simulator which has the simulation capability of high velocity and acceleration. Finally, a vehicle test including DGPS is executed to conform the real improvement of that filter performance. This filter can be applied to various data measurement systems to improve accuracy in high dynamic conditions besides GPS receiver.

• International Journal of Control, Automation, and Systems
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• 제1권4호
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• pp.444-452
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• 2003

An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.

• 한국군사과학기술학회지
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• 제1권1호
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• pp.201-210
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• 1998

Inertial Navigation System(INS) can provide the vehicle position and velocity information using inertial sensor outputs without the use of external aids. Unfortunately INS navigation error increases with time due to inertial sensor errors, and therefore it is desirable to combine INS with external aids such as GPS, TACAN, OMEGA, and etc.. In this paper we propose an integration algorithm of commercial GPS/GLONASS and INS where an adaptive filter for signal processing of GPS/GLONASS receiver and the 12th order Kalman filter for aided strapdown INS(SDINS) we employed. Simulation results show that the proposed adaptive filter can effectively remove a randomly occurring abrupt jump due to sudden corruption of the received satellite signal and that the Kalman filter performs satisfactorily.

• 대한조선학회논문집
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• 제45권3호
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• pp.269-279
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• 2008

In this paper, an underwater navigation system with adaptive receding horizon Kalman filter (ARHKF) is studied. It is well known that incorrect statistical information and temporal disturbance invoke errors of any navigation systems with Kalman filter, which makes the autonomous navigation difficult in real underwater environment. In this context, two kinds of problems are herein considered. The first one is the development of an algorithm, which estimates the noise covariance of a linear discrete time-varying stochastic system. The second one is the implementation of ARHKF to underwater navigation systems. The performance of the derived estimation algorithm of noise covariance and the ARHKF are verified by simulation and experiment in the towing tank of Seoul National University.

• 한국항공운항학회지
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• 제24권2호
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• pp.1-10
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• 2016

A GPS/SDINS/ST(Star Tracker) integrated sensor algorithm is more robust than the GPS/SDINS and the ST/SDINS systems on exploration of other planets. Most of the advanced studies shown that GPS/SDINS/ST integrated sensor with centralized Kalman filter was more accurate than those 2 integrated systems. The system, however, consist of a single filter, it is vulnerable to defects on failed data. To improve the problem, we work out a study using federated Kalman filter(No-Reset mode) and centralized Kalman filter with adaptive measurement fusion which known as robustness on fault. The simulation results show that the debasing influences are reduced and the computation is enable at least 100Hz. Further researches that the initial calibration in accordance with observability and applying the exploration trajectory are needed.

• 한국ITS학회 논문지
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• 제12권4호
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• pp.76-86
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• 2013

기존의 와이파이 기반 측위 시스템에서 주로 사용되는 칼만필터와 파티클 필터는 실내공간의 구조적 특성을 반영하지 못해 정확도가 낮고, 계산 부하 또한 높기 때문에 휴대기기를 이용한 실내 측위에 적용하는데 한계를 지닌다. 이러한 한계를 극복하고자 본 논문은 와이파이 기반 측위 시스템을 위한 적응형 혼합필터를 제안한다. 제안된 필터는 칼만 필터의 일반적인 적용 체계를 활용하였으며, 적은 수의 파티클을 사용한 파티클 필터의 개념 또한 추가되었다. 제안된 필터는 일반 칼만 필터와는 달리 예측 가중치를 동적으로 변화시켜 동작하며, 위치 예측을 위한 파티클을 실내공간의 경로 네트워크상에 한정하는 특징을 지닌다. 검증결과 적응형 혼합 필터는 일반 칼만 필터에 비해 높은 정확도를 보이며, 일반 파티클 필터에 비해서도 정확도 및 계산시간의 측면에서 유의할만한 성능향상을 보였다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.259-264
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• 2006

It is well known that the uncertainty of the covariance parameters of the process noise (Q) and the observation errors (R) has a significant impact on Kalman filtering performance. Q and R influence the weight that the filter applies between the existing process information and the latest measurements. Errors in any of them may result in the filter being suboptimal or even cause it to diverge. The conventional way of determining Q and R requires good a priori knowledge of the process noises and measurement errors, which normally comes from intensive empirical analysis. Many adaptive methods have been developed to overcome the conventional Kalman filter's limitations. Starting from covariance matching principles, an innovative adaptive process noise scaling algorithm has been proposed in this paper. Without artificial or empirical parameters to be set, the proposed adaptive mechanism drives the filter autonomously to the optimal mode. The proposed algorithm has been tested using road test data, showing significant improvements to filtering performance.

• 제어로봇시스템학회논문지
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• 제19권8호
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• pp.702-708
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• 2013

Underwater TRN (Underwater Terrain Referenced Navigation) estimates an underwater vehicle state by measuring a distance between the vehicle and undersea terrain, and comparing it with the known terrain database. TRN belongs to absolute navigation methods, which are used to compensate a drift error of dead reckoning measurements such as IMU (Inertial Measurement Unit) or DVL (Doppler Velocity Log). However, underwater TRN is different to other absolute methods such as USBL (Ultra-Short Baseline) and LBL (Long Baseline), because TRN is independent of the external environment. As a magnetic-field-based navigation, TRN is a kind of geophysical navigation. This paper develops an EKF (Extended Kalman Filter) formulation for underwater TRN. A filter propagation part is composed by an inertial navigation system, and a filter update is executed with echo-sounder measurement. For large-initial-error cases, an adaptive EKF approach is also presented, to keep the filter be stable. At the end, simulation studies are given to verify the performance of the proposed TRN filter. With simplified sensor and terrain database models, the simulation results show that the underwater TRN could support conventional underwater navigation methods.

• 전기학회논문지
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• 제68권1호
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• pp.108-121
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• 2019

This paper presents the study results on the design of the correction filter to improve the azimuth error estimation of the high-speed guided missile launched from the warship after the transfer alignment. We theoretically proved that the transfer alignment performance is determined by the accuracy of the marine inertial navigation system and the observability of the attitude error state variable in the transfer alignment filter, and that most of navigation errors in high-speed guided missile are caused by azimuth error. In order to improve the azimuth estimation performance of the correction filter, the multiple adaptive estimation method and the adaptive filters adapting the measurement noise covariance or the process noise covariance are proposed. The azimuth estimation performance of the proposed adaptive filter and the existing Kalman filter are compared and analyzed each other for 8 different transfer alignment accuracy cases. As a result of comparison and analysis, it was confirmed that the adaptive filter adapting the process noise covariance has the best azimuth estimation performance. These results can be applied to the design of correction filters for high-speed guided missile.

• 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.