• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.967-972
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• 2012

In order to implement good control of the permanent magnet synchronous motor(PMSM), the exact speed and rotor position information is needed.Recently, many studies have performed about sensorless speed control of the PMSM. This paper proposed sensorless speed controls of PMSM by using the Unscented Kalman Filter(UKF).The UKF is designed to eliminate the noise and get to the accuracy value and deals with the estimation of the speed and the rotor position of PMSM. Simulation and experiment have been performed for the verification of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.70-77
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• 2015

This paper proposes an application of unscented Kalman filter(UKF) for localization of an underwater robot. The method compares the bathymetric measurement from the robot with the seabed terrain information. For the measurement of bathymetric range to seabed, it uses a DVL which typically yields four range data together with velocity of the robot. Usual extended Kalman filter is not appropriated for application in case of terrain navigation, since it is not feasible to derive Jacobian for the bathymetric range measurement. Though particle filter(PF) is a nice solution which doesn't require Jacobian and can deal with non-linear and non-Gaussian system and measurement, it suffers from heavy computational burden. The paper compares the localization performance and the computation time of the UKF approach and PF approach. Though there have been some UKF methods which are used for underwater navigation, application of the UKF for bathymetric localization is rare. Especially, the proposed method uses only four range data whereas many of the bathymetric navigation methods have used multibeam sonar which yields hundreds of scanned range data. The result shows feasibility of the UKF approach for terrain-based navigation using small numbers of range data.

• Journal of Advanced Navigation Technology
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• v.20 no.1
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• pp.53-58
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• 2016

In this paper, we propose a novel quadratic Kalman filter based object tracking algorithm using moving pictures. Quadratic Kalman filter, which is introduced recently, has not yet been applied to the problem of 3-dimensional (3-D) object tracking. Since the mapping of a position in 2-D moving pictures into a 3-D world involves non-linear transformation, appropriate algorithm must be chosen for object tracking. In this situation, the quadratic Kalman filter can achieve better accuracy than extended Kalman filter. Under the same conditions, we compare extended Kalman filter, unscented Kalman filter and sequential importance resampling particle filter together with the proposed scheme. In conculsion, the proposed scheme decreases the divergence rate by half compared with the scheme based on extended Kalman filter and improves the accuracy by about 1% in comparison with the one based on unscented Kalman filter.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.808-811
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• 2020

This paper proposes a method to minimize the error of the Kinect camera sensor by using a random undirected Kalman filter. Kinect cameras, which provide RGB values and depth information, cause nonlinear errors in the sensor, causing problems in various applications such as skeleton detection. Conventional methods have tried to remove errors by using various filtering techniques. However, there is a limit to removing nonlinear noise effectively. Therefore, in this paper, a randomized unscented Kalman filter was applied to predict and update the nonlinear noise characteristics, we next tried to enhance a performance of skeleton detection. The experimental results confirmed that the proposed method is superior to the conventional method in quantitative results and reconstructed images on 3D space.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.174-180
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• 2016

This paper study about an unscented Kalman filter with a variety type of unscented transformation to estimate state values for speed control without position sensor of a permanent-magnet synchronous motor. The principles of an unscented transformation and unscented Kalman filter are examined and their application is explained. Generally the mapping process can be divided into two type, such as a basic and a general form according to a scaling parameter. And computation time, the number of samples, and weights about samples are different from each other. But, there is no little information on the scaling parameter value how this value influences the system performance. Simulation and experimental results show the validity of the designed unscented transformation performance with the various scaling parameter values for sensorless motor drive.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.4
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• pp.285-291
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• 2012

The paper experimentally verifies the performance of Terrain-Aided Navigation (TAN) using an interferometric radio altimeter, which is recently used due to its accuracy. First, we propose a TAN system that utilizes an interferometric radio altimeter as a measurement system. Second, we implement extended Kalman filter, unscented Kalman filter, and particle filter to evaluate the performance of TAN according to the selection of filters and the difference of environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.361-368
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• 2015

This paper reports an unscented Kalman filter approach for localization of a mobile robot in an indoor environment. The method proposes a new model of measurement uncertainty which adjusts the error covariance according to the measured distance. The method also uses non-zero off diagonal values in error covariance matrices of motion uncertainty and measurement uncertainty. The method is tested through experiments in an indoor environment of 100*40 m working space using a differential drive robot which uses Laser range finder as an exteroceptive sensor. The results compare the localization performance of the proposed method with the conventional method which doesn't use adaptive measurement uncertainty model. Also, the experiment verifies the improvement due to non-zero off diagonal elements in covariance matrices. This paper contributes to implementing and evaluating a practical UKF approach for mobile robot localization.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.265-274
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• 2019

The paper describes an application of unscented Kalman filter(UKF) for attitude estimation of an unmanned vehicle(UV), which is equipped with a low-cost attitude heading reference system (AHRS). The roll, pitch and yaw required at the correction stage of the UKF are calculated from the measurements of acceleration and geomagnetic field. The roll and pitch are attributed to the measurement of acceleration, while yaw is calculated from the geomagnetic field measurement. Since the measurement of geomagnetic field is vulnerable to distortion by hard-iron and soft-iron effects, the calculated yaw has more uncertainty than the calculated roll and pitch. To reduce the uncertainty of geomagnetic field measurement, the proposed method estimates bias in the geomagnetic field measurement and compensates for the bias for more accurate calculation of yaw. The proposed method is verified through navigation experiments of a UV in a test pool. The results show that the proposed method yields more accurate attitude estimation; thus, it results more accurate location estimation.

• Smart Media Journal
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• v.13 no.5
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• pp.9-18
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• 2024

Multi-Object trajectory modeling is a major challenge in MOT. CenterTrack tried to solve this problem with a Heatmap-based method that tracks the object center position. However, it showed limited performance when tracking objects with complex movements and nonlinearities. Considering the degradation factor of CenterTrack as the dynamic movement of pedestrians, we integrated the EKF into CenterTrack. To demonstrate the superiority of our proposed method, we applied the existing KF and UKF to CenterTrack and compared and evaluated it on various datasets. The experimental results confirmed that when EKF was integrated into CenterTrack, it achieved 73.7% MOTA, making it the most suitable filter for CenterTrack.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.3
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• pp.219-229
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• 2018

This paper proposes Kalman Filter-based relative navigation algorithms for proximity tasks such as rendezvous/docking/cluster-operation of spacecraft using PSD Sensors and Infrared Beacon Modules. Numerical simulations are performed for comparative analysis of the performance of each relative-navigation technique. Based on the operation principle and optical modeling of the PSD Sensor and the Infrared Beacon Module used in the relative navigation algorithm, a measurement model for the Kalman filter is constructed. The Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF) are used as probabilistic relative navigation based on measurement fusion to utilize kinematics and dynamics information on translational and rotation motions of satellites. Relative position and relative attitude estimation performance of two filters is compared. Especially, through the simulation of various scenarios, performance changes are also investigated depending on the number of PSD Sensors and IR Beacons in target and chaser satellites.