• 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 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 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.12
• /
• pp.1032-1039
• /
• 2012

The present paper deals with precise relative positioning of formation satellites with long baseline in low Earth orbit making use of L1/L2 dual frequency GPS carrier phase measurements. Kinematic approach means to describe the motion of objects without taking its mass/dynamics model into consideration. The advantage of the kinematic approach is that information about dynamics of the system is not applied, which gives more flexibility and could improve the scientific interest of the observations made by the mission. The ionosphere terms, which are not canceled by double differenced measurement equation in the case of the long baseline, are explicitly estimated as unknown parameters by extended Kalman filter. The estimated float ambiguities by EKF are solved by existing efficient integer vector search strategy under integer least square condition. For the integer vector search, we employ well known MLAMBDA. Finally, The feasibility and accuracy of processing scheme are demonstrated using the GPS measurements for two satellites in low Earth orbit separated by baselines of 100 km.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.10
• /
• pp.609-617
• /
• 2016

In general, Personal Navigation Systems (PNSs) can be defined systems to acquire pedestrian positional information. GPS is an example of PNS. However, GPS can only be used where the GPS signal can be received. Pedestrian Dead Reckoning (PDR) can estimate the positional information of pedestrians using Inertial Measurement Unit (IMU). Therefore, PDR can be used for GPS-disabled areas. This paper proposes a PDR scheme considering various movement types over GPS-disabled areas as combat environments. We propose a movement distance estimation scheme and movement direction estimation scheme as pedestrian's various movement types such as walking, running and crawling using IMU. Also, we propose a fusion algorithm between GPS and PDR to mitigate the lack of accuracy of positional information at the entrance to the building. The proposed algorithm has been tested in a real test bed. In the experimental results, the proposed algorithms exhibited an average position error distance of 5.64m and position error rate in goal point of 3.41% as a pedestrian traveled 0.6km.