• International Journal of Control, Automation, and Systems
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• v.1 no.4
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.524-532
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• 2002

This paper presents a new in-flight alignment method for an SDINS under large initial heading error. To handle large heading error, a new attitude error model is introduced. The attitude errors are divided into heading error and leveling errors using a newly defined horizontal frame. Some navigation error dynamic models are derived from the attitude error model for indirect feedback filtering of the in-flight alignment system. A Kalman filter with Position measurement is designed to estimate navigation errors as the indirect feedback filter Simulation results show that the proposed in-flight alignment method reduces the heading error very quickly from more than 40deg to about 5deg so as to apply a refined navigation filter. The total alignment process including leveling mode and navigation mode in addition to the proposed one allows large initial values not only in heading error but also in leveling errors.

• International Journal of Aeronautical and Space Sciences
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• v.2 no.1
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• pp.67-77
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• 2001

In recent navigation system, the profitable solution is to integrate the GPS and Stapdwon INS (SDINS) system and its integration allows compensation for shortcomings of each system. This paper describes the hardware preparation and presents the test results obtained from the automobile test of the developed system. The automobile tests was conducted with two kinds of inertial sensors and GPS receivers : short range and middle range test, to verify and evaluate the performance of the integrated navigation system. The reference of position is given by the Differential GPS(DGPS) which has cm-level accuracy to compare the accuracy of system. Kalman filtering is used for integrating GPS and SDINS and this filter effectively allows the long-term stability of GPS to correct and decrease the time deviation error of SDINS.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.3 s.22
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• pp.5-10
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• 2005

This paper presents the new in-flight alignment method using the flight distance of vehicle in order to improve the performance of the heading error estimation. In the proposed method, the Kalman filter having the difference between GPS and SDINS position as measurements is used for levelling of SDINS and heading error is estimated utilizing the flight distance information. It is shown in the simulation results that the in-flight method proposed in this paper has the high accuracy in heading error estimation and the heading error can be very quickly estimated at the high speed vehicle, compared with the existing method using the Kalman filter.

• Journal of the Korea Institute of Military Science and Technology
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• v.4 no.2
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• pp.60-69
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• 2001

전달정렬은 MINS와 SINS의 비정렬 요소를 칼만필터로 추정하는 기법으로서 본 논문에서는 속도 및 자세정합 칼만필터 설계 결과를 제시하였다. 그리고 설계된 칼만필터는 항법컴퓨터에 실현되어 성능평가가 실행되었는데 성능평가 방법은 실제 함정에서 시험의 어려운 점을 고려하여 실험실에 설치된 3축 각운동 시뮬레이터를 이용하는 방법을 제시하였다. 이러한 성능평가 결과는 전달정렬 설계과정에서 예측되었던 성능 및 특성과 일치하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.29-33
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• 1993

This paper suggests Kalman filter formulation using by precision GINS output angle for SDINS initial transfer alignment of missile. The Kalman filter model was derived from quaternion parameters and the transfer alignment system by angle matching method satisfies azimuth observability in horizontal angular motion. The estimated error of SDINS attitude settles to less 3mrad(1.sigma.) in 200 seconds at proper sea state.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.131-135
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• 1991

A quantization error model is suggested for analog to frequency(A/F) converter in strapdown inertial navigation system(SDINS),which is characterized by some white noise exciting the state variables. Also, effects on the performance of SDINS by analog to digital(A/D) converter and A/F converter are analyzed and compared via covariance simulation. As a result, A/F converter turns out to be superior to the A/D converter with respect to the induced navigation error and the difficulty in circuit realization. The quantization error model developed in this paper appears to be useful for optimal filter design.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.290-294
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• 1986

Initial alignment of gimballed INS is accomplished by the gyrocompassing techniques. These cannot be used in the case of strapdown system where the inertial instruments are directly strapped down to a vehicle frame. The basic objective of this paper is the development of digital methods performing the initial alignment of a SDINS from vibration and sway currupted data on the launch pad.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.61.2-61
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• 2001

The purpose of the initial alignment of a SDINS is to get a coordinate transformation matrix from the body frame to the navigation frame. The initial alignment is one of the most important processes in the navigation system since its error has a large influence on the navigation solution. In this paper, a real-time initial alignment algorithm for the SDINS is developed using the Kalman filter. The steady state error analysis is performed for the developed Kalman filter technique and the gyrocompass loop method. The performance of the developed alignment method is compared with the gyrocompass loop method through the real-time alignment experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.978-983
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• 2003

In this paper, we propose a fault detection method for extended Kalman filter in decentralized filter structure. To detect a fault, a consistency between filter output and a monitoring signal is tested. State propagators are used to obtain the monitoring signal. However, the output of state propagator increases in magnitude and finally diverges as time runs. To solve such problem, two-propagator method was proposed for linear system. Two propagators are reset by Kalman filter output, alternatively, to avoid divergence. But a test statistics change abruptly at the reset instant in that method. Hence a N-step propagator method is proposed to fix up the problem. In the N-step propagator, only time propagations are performed from k-N+1 step to k step without measurement updates. A test statistics are defined by errors and its covariance between extended Kalman filter and N-step propagator. These fault detection methods are applied to integrated strapdown inertial navigation system (SDINS). By computer simulation, it is shown that the proposed methods detect a fault effectively.