• 한국군사과학기술학회지
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• 제8권3호
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• pp.24-32
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• 2005

This paper describes the tracking filter performance for Naval Gun Ballistic Computation Unit(BCU). BCU needs tracing filter for gun firing. Using data of tracking sensor, BCU calculates the future position of Target and Gun order in the time of flight. In this paper, tracing filter is designed with interacting multiple model(IMM). The tracking algorithm based on the IMM requirers a considerable number of sub-model for the various maneuvering target in order to have a good performance. But, in the case of ship target, the maneuvering is restricted compared with the air target. Considering the maneuvering properties and adjusting the mode transition probabilities and the process noise of sub-model, We designed the IMM3 algorithm for Naval tracking filter with three sub-model.

• 한국군사과학기술학회지
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• 제18권5호
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• pp.517-524
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• 2015

In this paper, we propose a new target tracking filter architecture using active and passive sensors in underwater environment. A passive sensor for target tracking needs a bearing measurement of target. And target tracking filter for using passive sensor has the observability problem. On the other hand, an active sensor does not have the problem associated with system observability problem because an active sensor uses bearing and range measurement. In this paper, the tracking filter algorithm that could be used in the active and passive sensor system is proposed to analyze maneuvering target and to improve target tracking performance. The proposed tracking filter algorithm is tested by a series of computer simulation runs and the results are analyzed and compared with existing algorithm.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.563-566
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• 2004

An intelligent Kalman filter (IKF) is proposed for tracking an incoming anti-ship missile. In the proposed IKF, the unknown target acceleration is regarded as an additive process noise. When the target maneuver is occurred, the residual of the Kalman filter increases in proportion to its magnitude. From this fact, the overall process noise variance can be approximated from the filter residual and its variation at every sampling time. A fuzzy system is utilized to approximate this valiance, and the genetic algorithm (GA) is applied to optimize the fuzzy system. In computer simulations, the tracking performance of the proposed IKF is compared with those of conventional maneuvering target tracking methods.

• 전자공학회논문지
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• 제52권9호
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• pp.125-131
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• 2015

함정용 전자광학추적장비의 종속추적 표적지향 성능 개선을 위해 칼만 필터를 제안하였다. 전자광학추적장비의 추적기능 수행 시 표적지향 성능 저하의 주요 요인인 전송 지연 및 측정 오차를 칼만 필터를 활용할 경우 최소화할 수 있는 장점이 있다. 칼만 필터를 활용하여 방위각, 고각 방향으로의 표적지향 오차가 감쇄됨을 확인하고 전자광학추적장비에 적용해봄으로써 전자광학장비에서 빈번하게 발생하는 표적 추적 오차 개선 시스템으로 적합성을 제시하였다.

• 대한조선학회논문집
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• 제60권4호
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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.

• 한국해양공학회지
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• 제37권1호
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• pp.38-48
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• 2023

To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.

• International Journal of Control, Automation, and Systems
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• 제5권4호
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• pp.456-462
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• 2007

Tracking error performance is investigated for the typical maneuvering pattern of the anti-ship missile for tracking filters based on IMM filter in both clear and cluttered environments. Threatening targets to a navel vessel can be categorized into having three kinds of maneuvering patterns such as Waver, Pop-Up, and High-Diver maneuvers, which are classified according to launching platform or acceleration input to be applied. In this paper, the tracking errors for three kinds of maneuvering targets are represented and are investigated through simulation results. Studying estimation errors for each maneuvering target allows us to have insight into the most threatening maneuvering pattern and to construct the test maneuvering scenario for radar system validation.

• 한국군사과학기술학회지
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• 제17권1호
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• pp.96-107
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• 2014

Tracking the tactical object is a fundamental affair in network-equipped modern warfare. Geodetic coordinate system based on longitude, latitude, and height is suitable to represent the location of tactical objects considering multi platform data fusion. The motion of tactical object described as a dynamic model requires an appropriate filtering to overcome the system and measurement noise in acquiring information from multiple sensors. This paper introduces the filter suitable for multi-sensor data fusion and tactical object tracking, particularly the unscented transform(UT) and its detail. The UT in Unscented Kalman Filter(UKF) uses a few samples to estimate nonlinear-propagated statistic parameters, and UT has better performance and complexity than the conventional linearization method. We show the effects of UT-based filtering via simulation considering practical tactical object tracking scenario.

• 대한조선학회논문집
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• 제38권2호
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• pp.33-43
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• 2001

드릴쉽과 셔틀탱커등 특수선의 건조가 활발해지면서 정해진 위치에서 장시간 작업해야 할 목적으로 동적 자기위치 유지시스템(Dynamic Positioning System)을 장착하는 선박이 늘어나고 있다. 본 논문에서는 DP 시스템의 구성 요소를 소개하고, 수조에서 실험한 결과를 바탕으로 제어이론과 필터이론에 따른 DP성능을 비교하였다. 실험에 사용한 선박은 10만톤급 드릴쉽으로 모델의 길이가 4m이며, 방향이 고정된 3개의 추진기를 사용하였다. 실험 내용은 명령에 따라 주어진 궤적을 이동하는 능력과 파도가 있는 외란조건에서 원하는 선수각을 유지할 수 있는지를 살펴보았다. 추진력을 구하기 위하여 PID 이론을 적용하였고, 제어게인의 변화에 따른 제어특성을 살펴보았다. 선형운동성분과 계측잡음을 제거하기 위하여 칼만 필터와 디지털 필터를 적용하였고, 각각의 필터성능을 비교 검토하였다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.314-324
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• 2020

Due to the nonlinearity and environmental uncertainties, the design of the ship's steering controller is a long-term challenge. The purpose of this study is to design an intelligent autopilot based on Extended Kalman Filter (EKF) trained Radial Basis Function Neural Network (RBFNN) control algorithm. The newly developed free running model scaled surface vessel was employed to execute the motion control experiments. After describing the design of the EKF trained RBFNN autopilot, the performances of the proposed control system were investigated by conducting experiments using the physical model on lake and simulations using the corresponding mathematical model. The results demonstrate that the developed control system is feasible to be used for the ship's motion control in the presences of environmental disturbances. Moreover, in comparison with the Back-Propagation (BP) neural networks and Proportional-Derivative (PD) based control methods, the EKF RBFNN based control method shows better performance regarding course keeping and trajectory tracking.