• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.8
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• pp.640-647
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• 2018

This study analyzes the tracking accuracy(tracking errors) of fighter radar. Measurement error, detection failure, and radar cross section(RCS) fluctuation in radar measurements degrade the measurement quality and hence affect the tracking accuracy. Therefore, these radar measurement characteristics need to be considered when analyzing the tracking accuracy. In this paper, a method for analyzing the tracking accuracy is proposed; this method considers the detection error, detection probability, and RCS fluctuation. Results from experiments conducted with the proposed method show that the detection probability and RCS fluctuation affect tracking accuracy.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.3
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• pp.363-370
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• 2016

This paper proposes a Ku-band multi-mode coupler and its monopulse tracking system, which can be applied to a unmaned aircraft vehicle(UAV) platform. In general, the carrier-to-noise(C/N) level of the beacon signal from a Ku-band commercial satellite is relatively weak compared to that of a military satellite because the Ku-band satellite has been designed for commercial services. Therefore, this paper proposes a coupler and its multi-mode monopulse tracking system satisfying the tracking accuracy under a low C/N environment and analyzes the tracking accuracy. After that, we perform a real satellite tracking test and compare the accuracy of the test with the analysis result before validating the performance of the architecture of the proposed satellite tracking system.

• Journal of the Korean Solar Energy Society
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• v.36 no.6
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• pp.47-59
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• 2016

As the usage of sun tracking system in solar energy utilization facility increases, requirement of more accurate computation of sun position has also been increased. Accordingly, various algorithms to compute the sun position have been proposed in the literature and some of them insist that their algorithms guarantee less than 0.01 degree computational error. However, mostly, the true meaning of accuracy argued in their publication is not clearly explained. In addition to that, they do not clearly state under what condition the accuracy they proposed can be guaranteed. Such ambiguity may induce misunderstanding on the accuracy of the computed sun position and ultimately may make misguided notion on the actual sun tracking system's sun tracking accuracy. This work presents some comments related to the implementation of sun position computational algorithm for the sun tracking system. We first introduce the algorithms proposed in the literature. And then, from sun tracking system user's point of view, we explain the true meaning of accuracy of computed sun position. We also discuss how to select the proper algorithm for the actual implementation. We finally discuss how the input factors used in computation of sun position, like time, position etc, affect the computed sun position accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.77-82
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• 2010

The tracking performance of the big tracking antenna system using Radio Frequency is very important for the tracking and position measuring for the flight vehicle, but the precise measuring of the tracking performance is not easy, especially for the big antenna system such as ground telemetry antenna or tracking radar in space application because it's characteristics could be different in accordance with the antenna direction. In this paper, the error factors impacting on the tracking performance (pointing accuracy and tracking accuracy) and the ranges of each factor are reviewed, and the simple and efficient method to measure the tracking performance is introduced which using low earth orbit as the signal source. Finally, the measurement results for the telemetry ground antenna in NARO Space Center are reviewed.

• ETRI Journal
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• v.45 no.3
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• pp.394-403
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• 2023

Direct tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I-UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two-step tracking method.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1E
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• pp.25-29
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• 2008

The projection approximation subspace tracking (PAST) is one of the attractive subspace tracking algorithms, because it estimatesthe signal subspace adaptively and continuously. Furthermore, the computational complexity is relatively low. However, the algorithm still has room for improvement in the subspace estimation accuracy. In this paper, we propose a new algorithm to improve the subspace estimation accuracy using a normally ordered input vector and a reversely ordered input vector simultaneously.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.61.5-61
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• 2001

The laser tracking system(LTS) presents the most promising technique for dynamic position measurement of industrial robots. This system combine the advantage of high accuracy with a contactless measurement technique. It is the measurement system of position in three dimensions using distance data obtained by laser interferometer and real time angle by tracking mirror assembly. After measuring the tracking error of the beam projected on the center of retroreflector in robot end effector, this system tracks the end effector continuously by adjusting tracking mirror angle to minimize this error ...

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.710-726
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• 2018

This paper presents an extended particle filter to increase the accuracy and decrease the computation load of vehicle tracking. Particle filter has been the subject of extensive interest in video-based tracking which is capable of solving nonlinear and non-Gaussian problems. However, there still exist problems such as preventing unnecessary particle consumption, reducing the computational burden, and increasing the accuracy. We aim to increase the accuracy without an increase in computation load. In proposed method, we calculate the direction angle of the target vehicle. The angular difference between the direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted, based on their angular difference. Particles with angular difference greater than a threshold is eliminated and the remaining are stored with greater weights in order to increase their probability for state estimation. Threshold value is very critical for performance. Thus, instead of having a constant threshold value, proposed algorithm updates it online. The first advantage of our algorithm is that it prevents the system from failures caused by insufficient amount of particles. Second advantage is to reduce the risk of using unnecessary number of particles in tracking which causes computation load. Proposed algorithm is compared against camshift, direction-based particle filter and condensation algorithms. Results show that the proposed algorithm outperforms the other methods in terms of accuracy, tracking duration and particle consumption.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.707-720
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• 2014

This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.77-83
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• 2019

In this paper, we propose an object tracking algorithm based on color histogram and convolutional neural network model. In order to increase the tracking accuracy, we synthesize generic object tracking using regression network algorithm which is one of the convolutional neural network model-based tracking algorithms and a mean-shift tracking algorithm which is a color histogram-based algorithm. Both algorithms are classified through support vector machine and designed to select an algorithm with higher tracking accuracy. The mean-shift tracking algorithm tends to move the bounding box to a large range when the object tracking fails, thus we improve the accuracy by limiting the movement distance of the bounding box. Also, we improve the performance by initializing the tracking start positions of the two algorithms based on the average brightness and the histogram similarity. As a result, the overall accuracy of the proposed algorithm is 1.6% better than the existing generic object tracking using regression network algorithm.