• Journal of Advanced Marine Engineering and Technology
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• 제39권5호
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• pp.522-526
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• 2015

Generally, underwater unmanned vehicle have adopted an inertial navigation system (INS), dead reckoning (DR), acoustic navigation and geophysical navigation techniques as the navigation method because GPS does not work in deep underwater environment. Even if the tactical inertial sensor can provide very detail measurement during long operation time, it is not suitable to use the tactical inertial sensor for small size and low cost UUV because the tactical inertial sensor is expensive and large. One alternative to INS is attitude heading reference system (AHRS) with the micro-machined electro mechanical system (MEMS) inertial sensor because of MEMS inertial sensor's small size and low power requirement. A cost effective and small size attitude heading reference system (AHRS) which incorporates measurements from 3-axis micro-machined electro mechanical system (MEMS) gyroscopes, accelerometers, and 3-axis magnetometers has been developed to provide a complete attitude solution for UUV. The AHRS based MEMS overcome many problems that have inhibited the adoption of inertial system for small UUV such as cost, size and power consumption. Several evaluation experiments were carried out for the validation of the developed AHRS's function and these experiments results are presented. Experiments results prove the fact that the developed MEMS AHRS satisfied the required specification.

• International Journal of Aeronautical and Space Sciences
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• 제18권3호
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• pp.555-564
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• 2017

In this paper, a new technique for attitude and heading reference system (AHRS) using low-cost MEMS sensors of the gyroscope, accelerometer, and magnetometer is addressed particularly in vibration environments. The motion of MEMS sensors interact with the scale factor and cross-coupling errors to produce random errors by the harsh environment. A new adaptive attitude estimation algorithm based on the Kalman filter is developed to overcome these undesirable side effects by analyzing windowed measurement error covariance. The key idea is that performance degradation of accelerometers, for example, due to linear vibrations can be reduced by the proposed measurement error covariance analysis. The computed error covariance is utilized to the measurement covariance of Kalman filters adaptively. Finally, the proposed approach is verified by using numerical simulations and experiments in an acceleration phase and/or vibrating environments.

• 한국항공우주학회지
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• 제44권4호
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• pp.308-315
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• 2016

본 논문은 관성 항법 시스템(Inertial Navigation System)을 활용한 자세 및 방향 결정시스템(Attitude & Heading Reference System)의 성능을 향상시키기 위한 새로운 기법인, 순차적 측정 오차 공분산(Sequential Measurement Noise Covariance) 기법을 제시하였다. 관성 센서는 시간이 지남에 따라 발생하는 적분오차와 진동이나 가속구간과 같은 외란이 가해 졌을 때 성능이 저하된다는 단점이 있다. 특히, 저가의 관성 센서의 경우 이러한 현상이 더욱 두드러지게 나타난다. 이를 극복하기 위한 알고리즘들은 많이 존재한다. 하지만 가장 일반적으로 사용되는 확장 칼만 필터의 경우 가속도계를 사용할 때 측정값(Measurement)이 일정 범위를 넘어가면 센서값을 배제하는 방법을 사용한다. 본 논문에서 제안하는 기법은 범위를 설정하지 않고 과거의 데이터를 순차적으로 활용하여 측정값의 가중치를 변화하는 기법이다. 최종적으로 제안된 기법을 수치 시뮬레이션을 통해 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2077-2078
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• 2006

This paper describes the development or a closed-loop Strapdown Attitude Reference System (SARS) algorithm integrated filtering estimator for determining attitude reference for railway and aviation system using fuzzy inference. The SARS consists of 3 single-axis rate gyms in conjunction with 2 single-axis accelerometers. For optimal values of fuzzy systems, we utilize on-line scheduling method for initial values and then use genetic algorithms for fine tuning. Implementation using experimental test data of unmanned aerial vehicle has been performed in order to verify the estimation. The proposed fuzzy inference based SARS demonstrate that more accurate performance can be achieved in comparison with conventional one. The estimation results were compared with the on-board vertical gyro as the reference standard.

• Journal of Positioning, Navigation, and Timing
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• 제13권3호
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• pp.237-244
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• 2024

An Attitude and Heading Reference System (AHRS) provides the attitude of a vehicle with a constant velocity using an Inertial Measurement Unit (IMU) and a magnetometer. In this case, in order to avoid the disadvantage of a gyroscope, an AHRS using a Gyro-Free IMU (GF-IMU) that is composed of only accelerometers may be considered instead of the gyroscopes. In this paper, a design method of an AHRS using a GF-IMU is proposed. The proposed AHRS consists of roll and pitch calculation, yaw calculation, angular acceleration and angular velocity calculation, attitude calculation, and a Kalman filter. In particular, since the angular velocity cannot be measured from a gyroscope, the angular acceleration is obtained from the accelerometer output, and the angular velocity is calculated by integrating it. In order to show the usefulness of the proposed method, a performance evaluation was carried out. The performance evaluation results show that the attitude estimation performance of the proposed AHRS is similar to that of the conventional AHRS.

• 제어로봇시스템학회논문지
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• 제9권10호
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• pp.852-860
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• 2003

We introduce the error models for an attitude determination system(ADS) with gyroscopes and stellar sensor. The ADS error models are derived according to the definition of the reference frame and of the attitude error. The equivalent error models applicable to the attitude determination system with large attitude errors are presented. The simulation results show that the proposed error models improve performance of the attitude determination system.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2399-2410
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• 2017

In this paper, a linear matrix inequality-based sampled-data fuzzy observer design method is proposed based on the exact discretization approach. In the proposed design technique, a numerically relaxed observer design condition is obtained by using the discrete-time fuzzy Lyapunov function. Unlike the existing studies, the designed observer is robust to the uncertain premise variable because the fuzzy observer is designed under the imperfect premise matching condition, in which the membership functions of the system and observer are mismatched. In addition, we apply the proposed method to the state estimation problem of the attitude and heading reference system (AHRS). To do this, we derive a Takagi-Sugeno fuzzy model for the AHRS system, and validate the proposed method through the hardware experiment.

• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.222-231
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• 2016

This paper proposes a new method achieving computationally efficient attitude reference system for low cost strapdown sensors and microprocessor platform. The main idea in this method is to define and compare velocity differential vectors, geometrically computed from INS and GPS data with different update rate, for generating attitude error measurements which is further used for filter construction. A quaternion based Kalman filter configuration is applied for the attitude estimation with the adapted measurement model of differential vector comparison. Linearized model for Extended Kalman Filter and low pass filtered characteristics of measurement greatly extend the affordability of the proposed algorithm to the field of simple low cost embedded systems. For performance verification, experiment are done employing a practical low cost MEMS IMU and GPS receiver specification. Performance comparison with a high grade navigation system demonstrated good estimation result.

• 제어로봇시스템학회논문지
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• 제14권9호
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• pp.881-885
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• 2008

To calculate the attitude in ARS(Attitude Reference System) using 3 gyros and 3 accelerometers, gyro drift must be compensated with accelerometer to avoid divergence of attitude error. Kalman filter is most popular method to integrate those two sensor outputs. In this paper, new Kalman filtering method is proposed for roll and pitch attitude estimation. New states are defined to make linear equation and algorithm for changing Kalman filter parameters is proposed to ignore disturbances of acceleration. This algorithm can be easily applied to low cost ARS.

• 대한전기학회논문지
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• 제45권2호
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• pp.265-272
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• 1996

In this paper, attitude computation algorithm for a strap down ARS(Attitude Reference System)of an underwater vehicle has been studied. Attitude errors o the ARS using low-level gyroscopes tend to increase with time due to gyroscope errors. To cope with this problem, a mixing algorithm of accelerometer aided attitude computation has been developed. The algorithm can successfully bound the error increase for cruising motion, but it gives instantaneously large errors when a vehicle maneuvers. To improve the performance in case of vehicle's maneuver, a new attitude computation mixing algorithm complying state of vehicle and to manage the adjustment of the gains which are invariant in the existing algorithm. In addition, a gain scheduling method is applied to fuzzy inference composition process for real-time computation. Monte Carlo simulation results show that the proposed algorithm provides better performance than the existing algorithm.