• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1822-1829
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• 2008

Calibration of an MEMS inertial measurement unit is very important process for obtaining precise navigation performance. In this paper, one method is proposed to overcome a limitations on cost and efficiency using a relatively higher grade sensor and a rate table. The same dynamic input is applied to both the reference and the target sensors during and after calibration process, then the results are analyzed. The experimental results show that the proposed method is very effective and useful in practice.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.731-736
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• 2023

Walking is a periodic motion that contains specific phases and is a basic movement method for humans. Through gait analysis, various musculoskeletal health conditions can be identified. In this study, we propose a calf wearable sensor system that can perform gait analysis without space limitations. Using a ToF(: Time-of-Flight) sensor that measures distance and an IMU(: Inertial Measurement Unit) sensor that measures inclination the heel trajectory of walking was derived by proposed method. In case of abnormal gait with risk of fall, gait is evaluated by analyzing the change pattern of the heel trajectory.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.571-574
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• 1997

항법장치의 핵심요소인 가속도센서와 자이로센서는 선형거리추측(Linear position estimation)과 각 변위 추측(orientation estimation)시 출력 데이터에 포함된 오차성분의 적분에 의하여 시간이 증가함에 따라 선형거리 오차와 각 변위 오차가 누적된다. 이에 따라 본 논문에서는 정밀한 항법을 위한 저가의 IMU (Inertial Measurement Unit)를 설계하고, 오차성분의 사전해석을 통하여 정확한 오차모델을 찾는데 그 목적이 있다.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.53-62
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• 2017

In this paper, we present the score classification accuracy of BBS(Berg Balance Scale) which is the most commonly used balance evaluation tool using machine learning. Data acquisition was performed using the Noraxon system and an inertial sensor of Noraxon system was attached to the body in 8 locations (left and right ankle, left and right upper buttocks, left and right wrists, back, forehead). Based on the 3-axis accelerometer of the inertial sensor, the feature vector STFT(Short Time Fourier Transform) and SAM(Signal Area Magnitude) were extracted. Then, the items of the BBS were divided into static movement and dynamic movement depending on the operation characteristics, and the feature vectors were selected according to the sensor attachment positions which affect the score for each item of the BBS. Feature vectors selected for each item of BBS were classified using GMM(Gaussian Mixture Model). As a result of the accuracy calculation for 40 subjects, 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%, respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1120-1125
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• 2000

This paper presents a technique for estimating the attitude of a model helicopter at near hovering using a combination of inertial and non-inertial sensors such as gyroscope and potentiometer. To estimate the attitude of helicopter a simplified indirect Kalman filter based on sensor modeling is derived and the characteristics of sensors are studied, which are used in determining the optimal Kalman gain. To verify the effectiveness of the proposed algorithm simulation results are presented with real flight data. Our approach avoids a complex dynamic modeling of helicopter and allows for an elegant combination of various sensor data with different measurement frequencies. We also describe the method of implementation of the algorithm in the model helicopter.

• The Journal of Korea Robotics Society
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• v.7 no.4
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• pp.276-283
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• 2012

Due to the low localization accuracy and the requirement of special infrastructure, current LBS(Localization Based Service) is limited to show P.O.I.(Point of Interest) nearby. Improvement of IMU(Inertial Measurement Unit) based deadreckoning is presented in this paper. Additional sensors such as the magnetic compass and magnetic flux sensors are used as well as the accelerometer and the gyro for getting more information of movement. Based on the pedestrian movement, appropriate sensor information is selected and the complementary filter is used in order to enhance the accuracy of the localization.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.525-528
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• 2003

In this paper, we present a novel idea to integrate low cost Inertial Measurement Unit(IMU) and Differential Global Positioning System (DGPS) for Telematics applications. As well known, low cost IMU produces large positioning and attitude errors in very short time due to the poor quality of inertial sensor assembly. To conquer the limitation, we present a bimodal approach for integrating IMU and DGPS, taking advantage of positioning and orientation data calculated from CCD images based on photogrammetry and stereo-vision techniques. The positioning and orientation data from the photogrammetric approach are fed back into the Kalman filter to reduce and compensate IMU errors and improve the performance. Experimental results are presented to show the robustness of the proposed method that can provide accurate position and attitude information for extended period for non-aided GPS information.

• Journal of Sensor Science and Technology
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• v.16 no.4
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• pp.294-300
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• 2007

Autonomous unmanned vehicle is able to find the path and the way point by itself. For the more precise navigation performance, Extended kalman filter, which is integrated with inertial navigation system and global positioning system is proposed in this paper. Extended kalman filter's performance is evaluated by the simulation and applied to the unmanned vehicle. The test result shows the effectiveness of extended kalman filter for the navigation.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.1
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• pp.73-83
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• 2006

In this paper, a redundancy management system for aircraft is studied, and fault detection and isolation algorithms of inertial sensor system are proposed. Contrary to the conventional aircraft systems, UAV system cannot allow triple or quadruple hardware redundancy due to the limitations on space and weight. In the UAV system with dual sensors, it is very difficult to identify the faulty sensor. Also, conventional fault detection and isolation (FDI) method cannot isolate multiple faults in a triple redundancy system. In this paper, two FDI techniques are proposed. First, hardware based FDI technique is proposed, which combines a parity equation approach with a wavelet based technique. Second, analytic FDI technique based on the Kalman filter is proposed, which is a model-based FDI method utilizing the threshold value and the confirmation time. To provide the reference value for detecting the fault, residuals are calculated using the extended Kalman filter. To verify the effectiveness of the proposed FDI methods, numerical simulations are performed.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.884-887
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• 2023

Recently, research has been actively conducted to overcome the limitations of high-priced single sensors and reduce costs through the convergence of low-cost multi-variable sensors. This paper estimates state variables through asynchronous Kalman filters constructed using CVXPY and uses Cvxpylayers to compare and learn state variables estimated from CVXPY with true value data to estimate filter parameters of low-cost sensors fusion.