• Journal of Positioning, Navigation, and Timing
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• v.4 no.3
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• pp.107-114
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• 2015

Binary Offset Carrier (BOC) signal planned for future Global Navigation Satellite System (GNSS) provided better positioning accuracy and smaller multipath error than GPS C/A signal. However, due to the multiple side peaks in the auto-correlation function (ACF) of the BOC modulated signals, a receiver may false lock onto one of the side peaks in the tracking mode. This false lock would then result in a fatal tracking error. In this paper, we propose an unambiguous tracking method for composite BOC (CBOC) signals to mitigate this problem. It aims to reduce the side peaks of the ACF of CBOC modulated signals. It is based on the combination of traditional CBOC correlation function (CF) and reference CF of unmodulated pseudo- random noise code (PRN code). First, we present that cross-correlation function (CCF) with unmodulated PRN code is close to the secondary peaks of the traditional CBOC. Then, we obtain an unambiguous correlation function by subtracting traditional CBOC ACF from these CFs. Finally, the tracking performance for the CBOC signals is examined, and it is shown that the proposed method has better performance than the traditional unambiguous tracking method in additive white Gaussian noise (AWGN) channel.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.192-199
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• 2020

Recently, the utilization of the object tracking algorithm based on the deep learning model is increasing. A system for tracking multiple objects in an image is typically composed of a chain form of an object detection algorithm and an object tracking algorithm. However, chain-type systems composed of several modules require a high performance computing environment and have limitations in their application to actual applications. In this paper, we propose a method that enables real-time operation in low-performance computing environment by adjusting the computational process of object detection module in the object detection-tracking chain type system.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.9 s.174
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• pp.77-84
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• 2005

In most vision applications, we are frequently confronted with determining the position of object continuously. Generally, intertwined processes ire needed for target tracking, composed with tracking and control process. Each of these processes can be studied independently. In case of actual implementation we must consider the interaction between them to achieve robust performance. In this paper, the robust real time visual tracking in complex background is considered. A common approach to increase robustness of a tracking system is to use known geometric models (CAD model etc.) or to attach the marker. In case an object has arbitrary shape or it is difficult to attach the marker to object, we present a method to track the target easily as we set up the color and shape for a part of object previously. Robust detection can be achieved by integrating voting-based visual cues. Kalman filter is used to estimate the motion of moving object in 3D space, and this algorithm is tested in a pan/tilt robot system. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.126-130
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• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion (FBFE) based on virus evolutionary genetic algorithm(VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter (EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FBFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by identifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.632-638
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• 2011

In this paper, a nonlinear controller is proposed to track the desired velocity and to cancel sinusoidal disturbances. The proposed method consists of a velocity tracking controller and internal model principles (IMPs). For the design of the velocity tracking controller, mechanical and electrical dynamic controllers are independently designed. For the mechanical dynamics, the velocity tracking controller generates the desired quadrature current to track the desired velocity. The current tracking controller is designed to guarantee the desired quadrature current and to regulate the direct current. Therefore, the proposed velocity tracking controller has a field-oriented control. Since the controllers of the mechanical and electrical dynamics are independently designed, the stability of the closed-loop system is demonstrated using passivity. Since both the cogging torque and DC current errors act as sinusoidal disturbances in PMSM, we use four add-on type IMPs that preserve the merits and performance of the pre-designed controller without sacrificing the closed-loop stability. The performance of the proposed method is validated via simulations.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.1
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• pp.65-68
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• 2013

A photovoltaic array exhibits several local and single global maximum power points under partial shading conditions. To track the global maximum power point precisely, a novel global maximum power point tracking scheme is proposed in this paper. In the proposed scheme, robustness of the tracking performance has been improved by enhancing searching profile. In addition, the paper addresses the tracking failure condition, and provides the experimental verification with several simulation and experimental results.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• The Journal of the Acoustical Society of Korea
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• v.32 no.4
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• pp.354-360
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• 2013

In this paper, it is discussed that the detection and tracking performance of the piecewise linear path underwater target is improved using clutter reduction algorithm in heavy clutter density environment. Through clutter reduction algorithm using Hough Transform, measurements which represent clutter features are removed and the performance of target tracking on the remaining measurements is demonstrated applying CMKF-L(Converted Measurement Kalman Filter with Linearization) as tracking filter. Algorithm performance test is conducted using simulation data and real sea-trial data and by applying the proposed algorithm in heavy clutter density environment, it is confirmed that the target is tracked consistently and stably with clutter rejected measurements.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.4 s.19
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• pp.5-12
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• 2004

The tracking algorithm based on the interacting multiple model(IMM) requires a considerable number of sub-models for the various maneuvering targets in order to have a good performance. But it is not feasible to use the nm algorithm in the real system because of the computational burden. Therefore, we need an algorithm which requires less computing resources while maintaining a good performance. In this paper, we propose a fuzzy interacting multiple model algorithm(FIMMA) for the tracking of maneuvering targets, which uses a minimal number of sub-models by considering the maneuvering properties and adjusts the mode transition probabilities by using the mode probability as a fuzzy input. In order to verify the performance of FIMMA, the developed algorithm is applied to the tracking of i borne targets. Simulation results show that the FIMMA is very effective in the tracking of maneuvering targets.