• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.23 no.8
• /
• pp.995-1001
• /
• 2012

This paper compares the performance of several tracking filters, namely, alpha-beta filter, Kalman filter and TBM tracking filter for ballistic target tracking problem using multi-function radar. Every of three tracking filters suggested was tested on simulator developed in accordance with TBM trajectory and MFR RSP measurement. The result shows the method using TBM tracking filter gives 75.3 % decreased velocity RMS error than alpha-beta filter. After initialization, the RMS error of range and velocity of the proposed filter is also smaller than the Kalman filter. Finally the proposed filter is suitable for high-speed TBM tracking due to the stable angle tracking accuracy.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.8
• /
• pp.783-789
• /
• 2011

Target tracking performance improvement using multi-sensor data fusion is a challenging work. However, biases in the measurements should be removed before various data fusion techniques are applied. In this paper, a bias removing algorithm using measurement data from multi-radar tracking systems is proposed and evaluated by computer simulation. To predict bias estimation performance in various geometric relations between the radar systems and target, a system observability index is proposed and tested via computer simulation results. It is also studied that target tracking which utilizes multi-sensor data fusion with bias-removed measurements results in better performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.700-702
• /
• 2016

The Doppler Radar that mounted on Electro Optical Tracking System has been operated to measure range and velocity during the initial mission of space launch vehicle at Naro space center. In this paper, we mentioned configuration of MFCW(Multi frequency Continuous Wave) and FMCW(Frequency Modulation Continuous Wave) Doppler Radar on Electro Optical Tracking System and described method of range measurement.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.14 no.3
• /
• pp.517-523
• /
• 2011

The estimation filter in radar systems must track targets' position within low tracking error. In the Multi-Function Radar(MFR), ${\alpha}-{\beta}$ filter and Kalman filter are widely used to track single or multiple targets. However, due to target maneuvering, these filters may not reduce tracking error, therefore, may lost target tracks. In this paper, a target tracking filter based on particle filtering algorithm is proposed for the MFR. The advantage of this method is that it can track targets within low tracking error while targets maneuver and reduce impoverishment of particles by the proposed resampling method. From the simulation results, the improved tracking performance is obtained by the proposed filtering algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.99-99
• /
• 2000

An effect ive filter algorithm that can manage radar beam pointing efficiently is needed to track multi-target in the air. For effective beam management the filter has lobe good enough to predict future position of target and based on this filter output radar beam is control led to point toward the predicted target position in the air. In this paper, we investigate the ${\alpha}$-${\beta}$ filter known for its brief filter structure with the steady-state Kalman filter gain, the ruv filter, and the coordinate-transformed filter that can decouple the measurement noise variance.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.14 no.1
• /
• pp.106-113
• /
• 2011

This paper describes Data Acquisition & Analysis System(DAS) for analysis of the multi-function radar. There are various information - beam probing data, clutter map data, plot data, target tracking data, RT tracking data, radar signal processing data, interface data - this device saves. The most important thing of data analysis is that a researcher gets a view of the whole data. The DAS intergrates with all of the data and provides overall information on the time matters occur. This is very useful advantage for approaching the matter easily. System algorithms of multi-function radar are improved by using this advantage. As a result of, range blank region have fallen about 72% and it is able to keep track in jammer environment.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.4
• /
• pp.561-568
• /
• 2010

Instrumentation radar in a test range has an important role to measure target's TSPI(time, space, position, information). It is well known that it tracks a target stably using a beacon mode. But it may fail to track a target in a certain region using a beacon mode. In this paper, we modeled a simple missile shape similar to ATCMS with two beacon antenna and analyzed an antenna radiation pattern using MLFMM(Multi Level Fast Multipole Method) method. Using the analyzed result of the radiation pattern of the antenna and the attitude data of target, we simulated beacon tracking performance of an instrumentation radar. As a result of simulation, we showed that an instrumentation radar may lose the target because it tracks a area of the beacon antenna pattern.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.11
• /
• pp.1551-1558
• /
• 2014

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.24 no.2
• /
• pp.198-206
• /
• 2013

This paper presents the improvement of tracking performance using fuzzy inference system based task prioritizations for multi-function radars. The presented technique calculates elemental priorities using track information of a target and obtain the total priority from fuzzy inference system of each fuzzy set's membership function. In this paper, we proposed the task prioritization algorithms based on fuzzy inference system, and evaluated the tracking performance on multi-function radar scenario using it. As a result, we confirmed that excellent performance could be achieved when using the proposed algorithm.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.14 no.3
• /
• pp.405-413
• /
• 2011

Defense Systems Test Center in ADD supports increasingly various missile test requirements such as higher altitude event, multi target operation and low-altitude, high velocity target tracking. In this paper, we have proposed the development of instrumentation radar tracking status simulator based on virtual reality. This simulator can predict the tracking status and risk of failure using several modeling algorithms. It consists of target model, radar model, environment model and several algorithms includes the multipath interference effects. Simulation results show that the predict tracking status and signal are similar to the test results of the live flight test. This simulator predicts and analyze all of the status and critical parameters such as the optimal site location, servo response, optimal flight trajectory, LOS(Line of Sight). This simulator provides the mission plan with a powerful M&S tool to rehearse and analyze instrumentation tracking radar measurement plan for live flight test at DSTC(Defense Systems Test Center).