• Journal of international Conference on Electrical Machines and Systems
• /
• v.3 no.3
• /
• pp.272-275
• /
• 2014

An improved fuzzy Kalman filtering speed estimation scheme was proposed by means of measuring stator side voltage and current value based on vector control state equation of induction motor. The designed fuzzy adaptive controller conducted recursive online correction of measurement noise covariance matrix by monitoring the ratio of theory residuals and actual residuals to make it approach real noise level gradually, allowing the filter to perform optimal estimation to improve estimation accuracy of EKF. Meanwhile, co-simulation scheme based on MATLAB and Ansoft was proposed in order to improve simulation accuracy. Field-circuit coupling problems of induction motor under the action of vector control were solved and the parameter optimization accuracy was improved dramatically. The simulation and experimental results show that this algorithm has a strong ability to inhibit the random measurement noise. It is able to estimate motor speed accurately, and has superior static and dynamic characteristics.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.3
• /
• pp.315-327
• /
• 2017

In this paper, we present a method for estimating of position and velocity of a moving target by using the range and the bearing measurements from multiple sensors of aiming unit. In many cases, conventional low cost gyro sensor and a portable laser range finder(LRF) degrade the accuracy of estimation. To enhance these problems, we propose two methods. The first is background image tracking and the other is principal component analysis (PCA). The background tracking is used to assist the low cost gyro censor. And the PCA is used to cope with the problems of a portable LRF. In this paper, we prove that our method is robust with respect to low-frequency, biased and noisy inputs. We also present a comparison between our method and the extended Kalman filter(EKF).

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.43 no.2
• /
• pp.125-132
• /
• 2015

This paper is about analysis of UDRE monitoring method for Korean Satellite navigation system, which is the correction parameter of satellite measurements. New receiver clock bias and tropospheric delay error estimation method to make pseudo-range residual for UDRE monitoring is proposed. Saastamoinen model and Neill mapping function are used for estimate the tropospheric delay and EKF is used for estimgate the receiver clock bias. Through the satellite measurements and regional weather data received directly from the domestic is using for UDRE monitoring analysis, more suitable UDRE monitoring threshold can be deducted and it is expected to be utilized for fault detection technique of Korean Satellite Navigation System.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.4
• /
• pp.285-291
• /
• 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 Institute of Electronics Engineers of Korea SC
• /
• v.42 no.2 s.302
• /
• pp.1-10
• /
• 2005

This paper presents a technique for localization of a mobile robot using a single ultrasonic sensor. The mobile robot is designed for operating in a well-structured environment that can be represented by planes, edges, corners and cylinders in the view of structural features. In the case of ultrasonic sensors, these features have the range information in the form of the arc of a circle that is generally named as RCD (Region of Constant Depth). Localization is the continual provision of a knowledge of position which is deduced from it's a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. we defines a physically-based sonar sensor model and employs an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.

• Journal of the Korean Society for Railway
• /
• v.18 no.5
• /
• pp.447-456
• /
• 2015

Continuous as well as discrete measurement of the track geometry based on a track trolley are investigated to enhance the efficiency of the trolley and to minimize the measurement errors. A new kinematic model based on the track coordinates involving transition and circular curves is developed to improve the accuracy of the measurement; a nonlinear Extended Kalman Filter (EKF) is employed to linearize the governing equations. The proposed model is verified with the ideal track geometry in terms of both discrete and continuous measurement. Comparison with the previous models is also made to prove the applicability of the kinematic model.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.6
• /
• pp.219-230
• /
• 2017

This paper proposes a vehicle to road tracking algorithm based on vision sensor by using EKF(Extended Kalman Filter). The lateral offset, heading angle, and curvature which are obtained from vehicle to road tracking might be used as inputs to steering controller of LKAS(Lane Keeping Assist System) or for the warning decision logic of LDWS(Lane Departure Warning System). To the end, in this paper, the yaw rate, steering angle, and vehicle speed as well as lane raw points together with considering of vehicle lateral dynamics are utilized to improve the exactness and convergence of the vehicle to road tracking. The proposed algorithm has been tested at a proving ground that consists of straight and curve sections and compared with GPS datum using DGPS-RTK equipment to show the feasibility of the proposed algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.103-107
• /
• 2006

At present, most efforts tend to develop a INS which is only based linear accelerometers, because of the low cost micro-machining gyroscopes lack of the accuracy needed for precise navigation application and possible achieving the required levels of precise for micro-machining accelerometer. Although it was known in theory that a minimum of six accelerometers are required for a complete description of a rigid body motion, and any configuration of six accelerometers (except for a "measure zero " set of six-accelerometer schemes) will work. Studies on the feasible configuration of GF-INS indicate that the errors of angular velocity resolved from the six accelerometers scheme are diverged with time or have multi solutions. The angular velocity errors are induced by the biases together with the position vectors of the accelerometers, therefore, in order to treat with the problem just mentioned, researchers have been doing many efforts, such as the extra three accelerometers or the magnetometers may be taken as the reference information, the extended Kalman filter (EKF) involved to make the angular velocity errors bound and be estimated, and so on. In this paper, the typical configurations of GF-INS are introduced; for each type GF-INS described, the solutions to the angular velocity and the specific force are derived and the characteristic is indicated; one of the corresponding extend Kalman filters are introduced to estimate the angular errors; parts of the simulation results are presented to verify the validity of the equations of angular velocity and specific force and the performance of extend Kalman filter.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.2
• /
• pp.197-206
• /
• 2019

Safe aircraft requires highly reliable navigation information. The traditionally used inertial navigation system (INS) often displays faulty location information due to its innate errors. To overcome this, the INS/GNSS or INS/TRN integrated navigation can be used. However, GNSS is vulnerable to jamming and spoofing, while TRN can be degraded in the flat and repetitive terrains. In this paper, to improve the performance and ensure the high reliability of the navigation system, the INS/GNSS/TRN integrated navigation based on federated filter is designed. Master filter of the integrated navigation uses the estimates and covariances of two local filters - INS/GNSS and INS/TRN integrated filters. The local filters are designed with the EKF that is feedforward type and composed of the 17st state variables. And the INS/GNSS integrated navigation includes the barometer error compensation method. Finally, the proposed INS/GNSS/TRN integrated navigation is verified by vehicle and captive flight tests.

• Journal of Auto-vehicle Safety Association
• /
• v.13 no.4
• /
• pp.14-19
• /
• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.