• Journal of electromagnetic engineering and science
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• v.18 no.4
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• pp.231-241
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• 2018

In this paper, we consider the anti-attack procedure of a ballistic missile defense system (BMDS) at different operating frequencies at its phased-array radar station. The interception performance is measured in terms of lateral divert (LD), which denotes the minimum acceleration amount available in an interceptor to compensate for prediction error for a successful intercept. Dependence of the frequency on estimation accuracy that leads directly to prediction error is taken into account, in terms of angular measurement noises. The estimation extraction is performed by means of an extended Kalman filter (EKF), considering two typical re-entry trajectories of a non-maneuvering ballistic missile (BM). The simulation results show better performance at higher frequency for both tracking and intercepting aspects.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.2 s.25
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• pp.77-86
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• 2006

Usually, a tracking-gate of the tracker is used to track the target radar signal in the active ECM system. In this paper, we propose the feed-back tracking-gate filtering method. The designed method applies a tracking-gate of the tacker to the ECM system's receiver as a rejection or pass filter selected by the receiver's purpose, and the specific target signals can be passed or rejected though this tracking-gate filter. Thus, the number of input signals within the receiver's search band is minimized owing to this filter except the target signals. In conclusion, the EW equipment's reaction time can be reduced and the error value about the target signals can be lower than the previous methods'.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.26-29
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• 2001

In a target tracking problem the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In most nonlinear situations an Extended Robust Kalman Filter(ERKF) can yield acceptable performance as long as the noises are white Gaussian. However, an Extended Robust $H_{\infty}$ Filter (ERHF) can yield acceptable performance when the noises are Laplacian. In this paper, we use the Interacting Multiple Model(IMM) estimator for the problem of target tracking with glint noise. In the IMM method, two filters(ERKF and ERHF) are used in parallel to estimate the state. Computer simulations of a real target tracking shows that hybrid filter used the IMM algorithm has superior performance than a single type filter.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.457-462
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• 1990

Lately, at an epock of full-scale satellite ranching plan of Korea, T.T.C (Tracking, Telemetery & Command) is a indispensable part. In this paper, particular attention is given to orbit determination problem of role of T.T.C. Orbit determination, which is applied to Kalman Filter and Smoothing Filter, use the observation data which is given by satellite tracking radar system, and then the simulation is accomplished. As a result, it shows effectiveness.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.59-66
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• 1993

In this paper we propose an adaptive update rate tracking algorithm for a phased array radar, based on the interacting multiple model(IMM) algorithm. The purpose of the IMM algorithm hers is twofold: 1) to estimate and predict the target states, and 2) to estimate the level of the process noise. Using the estimate of the process noise level adapted to target dynamics, the update interval is determined to maintain a desired prediction accuracy so that the radar system load is minimized. The adaptive update rate tracking algorithm is implemented for a phased array radar and evaluated with Monte Carlo simulations on various trajectories. The evaluation results of the proposed algorithm and a standard Kalman filter without the adaptive update rate control are presented to compare.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.4
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• pp.248-255
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• 2023

In order to implement the autonomous navigation function, it is essential to track an object within a certain radius of the ship's route. This paper proposes the Multiple Probabilistic Data Association Filter (MPDAF), which can track multiple ships by extending Probabilistic Data Association Filter (PDAF), an existing single object tracking algorithm, using radar data obtained from real marine environments. The proposed MPDAF algorithm was developed to address the problem of tracking multiple objects in a complex environment where there can be significant uncertainty in the number and identification of objects to be tracked. Using real-world radar data provided by the German aerospace center (DLR), it has been verified that the proposed algorithm can track a large number of objects with a small position error.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.218-232
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• 2022

Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.583-597
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• 2001

Tracking the targets of interest has been one of the major research areas in radar surveillance system. We formulate the tracking problem as an incomplete data problem and apply the EM algorithm to obtain the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The difference is that the measurement-update deals with multiple measurements and the parameter-update can estimate the system parameters. Through extensive experiments, it turns out that the proposed system is better than PDAF and NNF in tracking the targets. Also, the performance degrades gracefully as the disturbances become stronger.

• Journal of Navigation and Port Research
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• v.39 no.4
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• pp.307-312
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• 2015

ARPA(Automatic Radar Plotting Aid) is a device to calculate CPA(closest point of approach)/TCPA(time of CPA), true course and speed of targets by vector operation of relative courses and speeds. The purpose of this study is to develop target acquisition and tracking technology for ARPA Radar implementation. After examining the previous studies, applicable algorithms and technologies were developed to be combined and basic ARPA functions were developed as a result. As for main research contents, the sequential image processing technology such as combination of grayscale conversion, gaussian smoothing, binary image conversion and labeling was deviced to achieve a proper target acquisition, and the NNS(Nearest Neighbor Search) algorithm was appllied to identify which target came from the previous image and finally Kalman Filter was used to calculate true course and speed of targets as an analysis of target behavior. Also all technologies stated above were implemented as a SW program and installed onboard, and verified the basic ARPA functions to be operable in practical use through onboard test.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.506-509
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• 2005

An adaptive ${\alpha}-{\beta}$ tracker is proposed for tracking maneuvering targets with a track-while-scan radar system. The tracker gain is updated on-line corresponding to the adjusted process noise variance which is obtained via time averaging of the process over a sliding window. The adjusted process noise variance is used to compute the maneuverability index for the tracker gain based on the steady-state Kalman filter equation for each epoch. It is shown via simulation that the proposed approach provides robust and accurate position estimates during the target maneuver while the performance of the conventional ${\alpha}-{\beta}$ tracker is shown much degraded.