• 제어로봇시스템학회논문지
• /
• 제21권6호
• /
• pp.547-552
• /
• 2015

In this paper, an ARS-EKF based motion counting algorithm for repetitive exercises such as calisthenics is proposed using a smartwatch. Raw sensor signals from accelerometers and gyroscopes are widely used for conventional smartwatch counting algorithms based on pattern recognition. However, generated features from raw data are not intuitive to reflect the movement of motions. The proposed motion counter algorithm is composed of navigation based feature generation and counting with error correction. The candidate features for each activity are velocity and attitude calculated through an ARS-EKF algorithm. In order to select those features which reveal the characteristics of each motion, an exercise frame from the initial sensor frame is introduced. Counting processes are basically based on the zero crossing method, and misdetected counts are eliminated via simple classification algorithms considering the frequency of the counted motions. Experimental results show that the proposed algorithm efficiently and accurately counts the number of exercises.

• Journal of information and communication convergence engineering
• /
• 제7권2호
• /
• pp.119-124
• /
• 2009

In this paper, we propose a novel approach to estimating the real-time moving trajectory of an object is proposed in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the input-output relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제10권2호
• /
• pp.101-106
• /
• 2010

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Journal of Positioning, Navigation, and Timing
• /
• 제12권3호
• /
• pp.271-280
• /
• 2023

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
• /
• pp.425-430
• /
• 1991

For satellite orbit determination, a satellite (K-3H) which is affected by the earth's gravitational field and the earth's atmospheric drag, the sun, and the moon is chosen as a dynamic model. The state vector include orbit parameters, uncertain parameters associated with perturbations and tracking stations. These perturbations include gravitational constant, atmospheric drag, and jonal harmonics due to the earth nonsphericity. Early orbit was obtained with given the predicted orbital parameter of the satellite. And orbit determination, which is applied to Extended Kalman Filter(EKF) for real time implementation , use the observation data which is given by satellite tracking radar system and then orbit estimation is accomplished. As a result, extended sequential estimation algorithm has a fast convergence and also indicate effectiveness for real time operation.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 2010년도 하계학술대회 논문집
• /
• pp.448-449
• /
• 2010

This paper proposes a sensorless control strategy for the Interior Permanent Magnet Synchronous Motor (IPMSM) by using the parallel reduced-order Extended Kalman Filter. The sensorless control strategy is composed with two EKFs alternately computed every sampling period with a new model. The new model is based on the extended electromotive force (EEMF) which has a simple structure, making position estimation possible without approximation. The proposed strategy can save computation time and estimate rotor speed and position. To verify the merit of the proposed strategy, simulation and experimental results validate the theoretical analysis and show the feasibility of the proposed control strategy.

• 제어로봇시스템학회논문지
• /
• 제20권7호
• /
• pp.706-712
• /
• 2014

The inverters used to drive Brushless DC motors (BLDC) include switching devices such as FETs and the faults in FETs cause severe performance degradation in systems where a BLDC acts as actuator. This paper presents a fault detection and isolation method for the FETs of an inverter for BLDC motor control systems, which is based on the EKF (Extended Kalman filter). Firstly, an equivalent circuit model for a BLDC motor plus its inverter system was derived. Secondly, a state-space equation was established, where the on-resistance of the FETs is expressed as a state variable and the EKF equation estimates the on-resistance. If the estimated resistance differs greatly from the known value, it can be asserted that there is a fault on that FET. Thirdly, the local convergence of the established EKF was proved. Finally, through the experiments, the performance of the proposed method was verified. The results show that the on-resistance is estimated close to the value specified in the FET data sheet in normal operation, whereas the estimated resistance is a much larger value than the normal one in case an FET fault occurs. Therefore, it is confirmed that the proposed fault detection and isolation method works appropriately in real systems.

• 한국음향학회:학술대회논문집
• /
• 한국음향학회 2004년도 추계학술발표대회논문집 제23권 2호
• /
• pp.479-482
• /
• 2004

In this article we consider a single maneuvering target Tracking algorithm in the presence of missing measurements and high clutter environments for multi-sensor target tracking problem. The tracking algorithm is based on the Particle filtering method to predict and update target states. Proposed is the AMM-PF(Auxiliary Multiple Model Particle Filter)[2] method for maneuvering target tracking to improve performance in track estimate and maintenance with a high level of uncertainty. The algorithm we propose is compared to the Extended Kalman Filter(EKF). A simulation study is included.

• Journal of Astronomy and Space Sciences
• /
• 제37권3호
• /
• pp.187-197
• /
• 2020

In order to avoid the high cost and high risk of demonstration mission of rendezvous-docking technology, missions using nanosatellites have recently been increasing. However, there are few successful mission cases due to many limitations of nanosatellites like small size, power limitation, and limited performances of sensor, thruster, and controller. To improve the probability of rendezvous-docking mission success using nanosatellite, a rendezvous-docking phase analysis tool for nanosatellites is developed. The tool serves to analyze the relative position and attitude control of the chaser satellite at the docking phase. In this tool, the Model Predictive Controller (MPC) is implemented as a controller, and Extended Kalman Filter (EKF) is adopted as a filter for noise filtering. To verify the performance and effectiveness of the developed tool for nanosatellites, simulation study was conducted. Consequently, we confirmed that this tool can be used for the analysis of relative position and attitude control for nanosatellites in the rendezvous-docking phase.