• Journal of Auto-vehicle Safety Association
• /
• v.13 no.4
• /
• pp.123-128
• /
• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• Nuclear Engineering and Technology
• /
• v.53 no.10
• /
• pp.3431-3437
• /
• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.5
• /
• pp.1083-1094
• /
• 2012

Commercialized location estimation System with high accuracy is widely used for various services. However, if the systems aren't completely installed in an indoor, location estimation accuracy tend to be very poor. In this paper, indoor location estimation algorithm to improve the accuracy of object location, by correcting the location information obtained from a system that does not fully install, is proposed. In this paper, UWB-based Ubisense system that provides high position accuracy in an indoor environment was utilized. In conclusion, this paper was able to improve the positioning accuracy, by correcting that information about the location of the measured object in position estimation system.

• Journal of Broadcast Engineering
• /
• v.24 no.1
• /
• pp.32-40
• /
• 2019

Indoor location estimation studies are needed in various fields. The method of estimating the indoor position can be divided into a method using hardware and a method using no hardware. The use of hardware is more accurate, but has the disadvantage of hardware installation costs. Conversely, the non-hardware method is not costly, but it is less accurate. To estimate the location for AR game, you need to get the solution of the Perspective-N-Point (PnP). To obtain the PnP problem, we need three-dimensional coordinates of the space in which we want to estimate the position and images taken in that space. The position can be estimated through six pairs of two-dimensional coordinates matching the three-dimensional coordinates. To further increase the accuracy of the solution, we proposed the use of an additional non-coplanarity degree to determine which points would increase accuracy. As the non-coplanarity degree increases, the accuracy of the position estimation becomes higher. The advantage of the proposed method is that it can be applied to all existing location estimation methods and that it has higher accuracy than hardware estimation.

• Journal of international Conference on Electrical Machines and Systems
• /
• v.2 no.2
• /
• pp.184-189
• /
• 2013

This paper proposes a method for improving the performance of a position sensorless control system for a slotless surface permanent magnet synchronous motor (SPMSM) in a very low-speed region. In position sensorless control based on a motor model, accurate motor parameters are required because parameter errors would affect position estimation accuracy. Therefore, online parameter identification is applied in the proposed system. The error between the reference voltage and the voltage applied to the motor is also affect position estimation accuracy and stability, thus it is compensated to ensure accuracy and stability of the sensorless control system. In this study, two voltage error compensation methods are used, and the effects of the compensation methods are discussed. The performance of the proposed sensorless control method is evaluated by experimental results.

• Journal of Magnetics
• /
• v.19 no.1
• /
• pp.90-100
• /
• 2014

Position estimation that uses only active phase voltage and current is presented, to perform high accuracy position sensorless control of a SRM drive. By extracting the amplitude of the first switching harmonic terms of phase voltage and current for a PWM period through Fast Fourier Transform (FFT), the flux-linkage and position are estimated without external hardware circuitry, such as a modulator and demodulator, which result in increased cost, as well as large position estimation error, produced when the motional back EMF is ignored near zero speed. A two-phase SRM drive system, consisting of an asymmetrical converter and a conventional closed-loop PI current controller, is utilized to validate the performance of the proposed position estimation scheme in comprehensive operating conditions. It is shown that the estimated values very closely track the actual values, in dynamic simulations and experiments.

• Journal of Advanced Navigation Technology
• /
• v.25 no.6
• /
• pp.441-449
• /
• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

• Journal of Sensor Science and Technology
• /
• v.31 no.3
• /
• pp.156-162
• /
• 2022

This study estimates the relative position between body segments using segment orientation and segment-to-joint center (S2J) vectors. In many wearable motion tracking technologies, the S2J vector is treated as a constant based on the assumption that rigid body segments are connected by a mechanical ball joint. However, human body segments are deformable non-rigid bodies, and they are connected via ligaments and tendons; therefore, the S2J vector should be determined as a time-varying vector, instead of a constant. In this regard, our previous study (2021) proposed a method for determining the time-varying S2J vector from the learning dataset using a regression method. Because that method uses a deformation-related variable to consider the deformation of S2J vectors, the optimal variable must be determined in terms of estimation accuracy by motion and segment. In this study, we investigated the effects of deformation-related variables on the estimation accuracy of the relative position. The experimental results showed that the estimation accuracy was the highest when the flexion and adduction angles of the shoulder and the flexion angles of the shoulder and elbow were selected as deformation-related variables for the sternum-to-upper arm and upper arm-to-forearm, respectively. Furthermore, the case with multiple deformation-related variables was superior by an average of 2.19 mm compared to the case with a single variable.

• Aerospace Engineering and Technology
• /
• v.6 no.1
• /
• pp.237-244
• /
• 2007

Estimation of target position is one of the main functions of surveillance UAVs, and is being used to various purposes but generally noisy target position is estimated due to the existence of random measurement errors. In this report, a method of diminishing target position estimation error by calculating target position using Kalman Filtered optimum values such as position, attitude of UAV and sight vector of optical instrument, is proposed.

• Journal of Power Electronics
• /
• v.9 no.3
• /
• pp.499-506
• /
• 2009

In this paper, the initial pole-position estimation of a surface (non-salient) permanent magnet synchronous motor is mathematically analyzed and surveyed on the basis of simulation analysis, and developed for accurate servo motor drive. This algorithm is well carried out under the full closed-loop position control without any pole sensors and is completely insensitive to any motor parameters. This estimation is based on the principle that the initial pole-position is simply calculated by the reverse trigonometric function using the two feedback currents in the full closed-loop position control. The proposed algorithm consists of the predefined reference position profile, the information of feedback currents, speed, and relative position, and the reverse trigonometric function for the initial-pole position estimation. Comparing with the existing researches, the mathematical analysis is introduced to get a more accurate initial pole-position of the surface permanent magnet motor under the closed-loop position control. It is found that the proposed algorithm can be easily applied in servo drive applications because it satisfies the following user's specifications; accuracy and moving distance.