• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.1
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• pp.49-59
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• 2011

Recently, ToA and TDoA estimation are favorable among all of estimation techniques because they have the best accuracy in estimating position. ToA and TDoA estimation are typical techniques based on time. So, it is important to have the time syncronization and offset between a target node and several reference nodes. If they don't have the time syncronization between a reference node and target node or have a time offset among reference nodes, the positioning error will increase due to the ranging error. The conventional positioning algorithm does not have a accurate device's position because ranging error is added the calc dation of the position. In this paper, we propose a hybrid method of ToA and TDoA ll increase due. We use MHP pulse that has orthogonal pulse instead of the existing pulse to transmit and receive pulses between a target node and reference nodes. We can estimate the target node's position by ToA and TDoA estimation to transmit and receive MHP pulses only once. When the proposed Hybrid method iteratively calculate the distance, we can select the ranging technique to have more accurate position. The simulation results confirm the enhancement of the Hybrid method.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.2
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• pp.202-209
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• 2014

In this paper, a developed device for detecting target's location and avoiding collision is proposed. Velocity and acceleration model of target are derived to estimate target's information, i.e. position, velocity and acceleration considering process and measurement noise. Kalman filtering method applied to the estimation process and its results was confirmed by simulation. The distance measurements system using laser sensor for moving target system is also developed to confirm the effectiveness of the proposed scheme. Experiments to get information of moving target with velocity and acceleration model was executed. The data with filtering and without filtering was compared by experiments. Discontinuous measured data was changed to smooth and continuous data by Kalman filtering. It is confirmed that desired data was obtained by applying proposed scheme. UI for measuring and monitoring the target data is developed and visual and auditory alarm function is attached on the system Finally, position estimation system of moving target with good performance is achieved by low price equipments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1121-1141
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• 2020

We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.562-565
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• 1987

This paper addresses the development of the estimation algorithm to acquire target position, velocity and course using bearing-only measurements in two dimensional environment. System state equations are derived from modified polar coordinates instead of existing Cartesian coordinates system. The Extended Kalman Filter is used to constitute the estimation algorithm because of state equation's nonlinearity. The computer simulation is done to verify the performance of derived algorithm. Simulation result showed that estimated state value of filter was converged to the true value in 10 minutes.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.524-527
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• 1997

Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.111-116
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• 2014

In this paper, we estimates the direction of arrival of desired a target using linear array antenna in wireless communication. Direction of arrival estimation is to estimate for desired target position among incident signals on receiver array antennas. This paper improved estimation of direction of arrival for target using optimum weight, high resolution adaptive beamforming algorithm, and sparse matrix for driection of arrival estimation. Through simulation, we showed that we are performance the analysis to compare general algorithm with proposed algorithm. We show that propose algorithm more improve for direction of estimation than general beamforming algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.11
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• pp.996-1003
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• 2016

Trilateration technique using time of arrival(TOA) is generally used for single target position estimation in radar system. However, trilateration technique has limitation in case of multiple targets, since it is difficult to distinguish the measurements corresponding to the respective targets. In this study, to eliminate ambiguity of relation between measurements and targets, micromotion of each target is measured by micro Doppler which is actively studied in radar industry nowadays and these information are used to distinguish measurements used at trilateration technique. Resultingly, the trilateration technique is applied successfully for each target. The targets are considered as multiple submissiles separated from the missile. Simulation results shows the performance of the proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.111-116
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• 2008

Radar system must estimate exactly quickness and target in interference channel. Because interference of radio channel is multipath channel by artificial structure and nature structure. signal estimation is difficult. As long as, get rid of interference signal have been study digital beamforming, adaptive array antenna and so on. In this paper, proposed SPT-SALCMV beamforming algorithm get rid of coherent interference algorithm and adaptive array antenna. Adaptive array forms null pattern and reduces gains for direction of interference signal. And estimate signal that want by keeping gains of beam pattern changelessly to target signal direction. In this paper, proposed SPT-SALCMV algorithm was exactly received position of target. But general SPT-LCMV algorithm resulted beam error about 30degrees. Therefore, proved that SPT-SALCMV algerian that propose in this paper is more excellent than genaral SPT-LCMV algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.121-130
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• 2000

A practical method is proposed to improve hit probability of the digital gun fire control system, when the measured rate of the tracking sensor becomes biased under some operational situation. For ground moving target it is shown that the well-known Kalman filter which uses position measurement only can be optimally used to eliminate the rate bias error. On the other hand, for 3D moving aircraft we present a new algorithm which incorporate FIR-type filter, which uses position and rate measurement at the same time, and the fixed-lag smoother using position measurement only, and show that it has the optimal performance in terms of both estimation accuracy and response time.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.49-54
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• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.