• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.10a
• /
• pp.598-601
• /
• 1995

This paper proposes a federated Kalman filter approach which utilizes information from multiple sensors and variable estimation model. Compared with the decentralized Kalman filter, the algorithm proposed in this paper demonstrates much better tracking performance in both maneuvering and constant velocity movement of the target.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.6
• /
• pp.107-112
• /
• 2014

In this paper, we analysed a performance signal to noise ratio about pulse, integration coherent, and integration non coherent system in radar system. It compared existing with proposal method in order to estimation two target direction of arrival. Generally, radar system radiate pulse wave in order to decreasing distortion of return wave and transmission wave. We analysed the performance integration coherent and integration non coherent. Integration coherent is processing system before doing envelop detection, and integration non coherent is processing system after doing envelop detection. Through simulation, we analysed a performance signal to noise ratio to estimation two target range detection and estimated target direction of arrival. We showed that integration coherent system is the most good performance.

• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.599-604
• /
• 2018

A Stepped Frequency Radar(SFR) is a method that realizes high resolution range estimation by increasing the frequency of transmission pulses at regular intervals to generate a wide synthetic bandwidth. However, in the case of a moving target, accurate range estimation becomes difficult due to the range-Doppler coupling. In this paper, the process of high resolution range estimation by compensation of the range-Doppler coupling with estimated velocity of the moving target using the SFR waveform with Coherent Pulse Train(CPT) is analyzed and it was verified through simulation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.5
• /
• pp.400-404
• /
• 2015

In this paper, we study for direction of arrival MUSIC spatial spectrum algorithm in order to desired signal estimation in spatial. Proposal MUSIC spatial spectrum algorithm in paper use model error and Bayesian method to estimation on correct target position. Receiver array response vector using adaptive array antenna use Bayesian method, and target position estimate to update weight value with model error method. Target's signal estimation of desired direction of arrival in this paper apply weight value of signal covariance matrix for array response vector after removing incident signal interference and noise, respectively. Though simulation, we analyze to compare proposed method with general method.

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

• Journal of the Korea Institute of Military Science and Technology
• /
• v.18 no.3
• /
• pp.253-259
• /
• 2015

This paper introduces a signal processing technique for discrimination of missile target. In order to detect and discriminate the target, a seeker radar makes use of chirp waveform and stretch processing to generate high resolution range profiles(HRRPs). RELAX(relaxation) algorithm, which is one of the spectral estimation techniques, was used to find scattering centers of a missile from HRRP. From the information on the distribution of one-dimensional(1-D) scattering centers on a target, we can discriminate the target without noise.

• The Journal of the Acoustical Society of Korea
• /
• v.36 no.5
• /
• pp.293-299
• /
• 2017

Small unmanned platforms maneuvering underwater are the key naval future forces, utilized as the asymmetric power in war. As a method of detecting and identifying such platforms, we introduce a property estimation technique based on an iterative numerical analysis. The property estimation technique can estimate not only the position of a target but also its physical properties. Moreover, it will have a potential in detecting and classifying still target or multiple targets. In this study, we have conducted the property estimation of an small underwater target using the data acquired from the lake experiment. As a result, it shows that the properties of a small platform may be roughly estimated from the in site data even using one channel.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.4929-4947
• /
• 2017

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.17 no.3
• /
• pp.364-377
• /
• 2014

In this paper, we have developed a target recognition algorithm based on a template matching technique using Synthetic Aperture Radar (SAR) images. For efficient computations, Radon transform-based azimuth estimation algorithm was used with the template matching. MSTAR data set was divided into two groups according to the depression angles, which were a train set and a test set. Template data were generated by rotating and cropping chips which were from MSTAR train set using the azimuth estimation algorithm. Then the template matching process between test data and template data was performed under various conditions. Performance variation according to contrast enhancement preprocessing which is scarce in open literature was also presented. The analysis results show that the target recognition algorithm could be useful for the automatic target recognition using SAR images.

• Journal of the Institute of Convergence Signal Processing
• /
• v.14 no.1
• /
• pp.33-38
• /
• 2013

In this paper, we propose an algorithm to track multi-small targets in infrared image sequences in case of dissipation or creation of targets by using the background estimation filter, Kahnan filter and mean shift algorithm. We detect target candidates in a still image by subtracting an original image from an background estimation image, and we track multi-targets by using Kahnan filter and target selection. At last, we adjust specific position of targets by using mean shift algorithm In the experiments, we compare the performance of each background estimation filters, and verified that proposed algorithm exhibits better performance compared to classic methods.