• Journal of Advanced Navigation Technology
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• v.4 no.1
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• pp.50-66
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• 2000

For a long time target maneuvers in tracking problem have been a difficult task to handle. Once a maneuvering such as abrupt change in target accelerations occur, the tracking fiter no longer yields a reasonable estimate of the target position. In order to resolve this cumbersome maneuvering problem. Advanced methods have here proposed : Colored noise, IE(Input Estimation), VD(Variable Dimension), IMM(Interaction Multiple Model), Jump-type processes and jerk model, etc. In this paper, tracking performance of the jerk model is analyzed. Jerk model in which the derivative of target acceleration is included as a state recently attracted considerable attraction. Firstly 3-dimensional Kalman filter is described on the basis of jerk model. Then using this filter, Monte-Carlo simulations are carried out and the filter formance with respect to the variation of jerk time-constant is analyzed. Especially, since jerk model's transient performance is expected to be poor, the performance of analysis of transient response of the model is included too.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.711-715
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• 2001

In this paper, an effective filter structure for filtering of target glint in tracking radar systems is used to improve the performance of CLOS(Command to Line-Of-Sight) guidance. The filter decouples range and angel channels to that it has a sound mathematical basis as well as computation efficiency as applied to the IMM algorithm. The effective filter structure in conjunction with CLOS guidance is tested by a series of simulation runs and it is shown to have superior performance compared with the other filter structures.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.290-291
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• 2007

Barometers can provide height information steady but its accuracy becomes poor as the air data varies due to the vehicles's moving or time's elapsing. In order to keep the accuracy in spite of the air data changes, we propose a filter for the identification of baro-errors. The baro-errors mainly consist of bias and scale factor errors which gradually varies as the air data varies. With GPS height measurements, the scale factor and bias estimator is designed by applying the interacting multiple model (IMM) filtering technique to the baro-error random walk model. The resultant estimates are used to compensate current baro-measurement to supply accurate measurements steadily.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.1
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• pp.25-32
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• 2011

Measurement noise variance of the radar is one of the main inputs of a state estimator of surveillance data processing system for air traffic control and has influences on the accuracy performance of maneuvering target tracking. A method is presented of estimating measurement noise variances every frame of target tracking using likelihood functions of multiple IMM filter. The results by running of Monte Carlo simulation show that variances are estimated within 5% of errors compared with true values and the tracking accuracy performance is improved.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.11-18
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• 2021

This paper shows the applicability of deep learning algorithm in predicting target position and getting correction value of impact point in order to improve the accuracy of naval gun firing. Predicting target position, the proposed model using LSTM model and RN structure is expected to be more accurate than existing method using kalman filter. Getting correction value of impact point, the another proposed model suggests a reinforcement model that manages factors which is related in ballistic calculation as data set, and learns using the data set. The model is expected to reduce error of naval gun firing. Combining two models, a ballistic calculation model for improving accuracy of naval gun firing based on deep learning algorithm was designed.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.26-33
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• 2001

Though speaker recognition based on conventional AR HMM shows good performance, its lack of modeling the environmental noise makes its performance degraded in case of practical noisy environment. In this paper, a robust speaker recognition system based on AR HMM is proposed, where noise is considered in the observation signal model for practical noisy environment and duration-term is considered to increase performance. Experimental results, using the digits database from 100 speakers (77 males and 23 females) under white noise and car noise, show improved performance.

• Smart Media Journal
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• v.5 no.2
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• pp.84-89
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• 2016

For a place-recommendation model based on user's behavior and multi-attribute attitude in this thesis. We focus groups that show similar patterns of visiting restaurants and then compare one and the other. We make use of The Fishbein Equation, Pearson's Correlation Coefficient to calculate multi-attribute attitude scores. Furthermore, We also make use of Preference Prediction Algorithm and Distance based method named "Euclidean Distance" to provide accurate results. We can demonstrate how excellent this system is through several experiments carried out with actual data.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.82-89
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• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.