• Journal of Mechanical Science and Technology
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• 제19권9호
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• pp.1742-1752
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• 2005

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

• 대한기계학회논문집A
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• 제29권1호
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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.

• 전기학회논문지
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• 제56권10호
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• pp.1826-1835
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• 2007

In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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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.

• 전자공학회논문지B
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• 제33B권6호
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• pp.186-197
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• 1996

A multiple target tracking (MTT) problem is to track a number of targets in clusttered environment, where measurements may contain uncertainties of measurement origin due to clutter, missed detection, or other targets, as well as measurement noise errors. Hence, an MTT filter should be introduced to resolve this problem. In this paper, a neural network is rpoposed as an MTT filter.

• 한국멀티미디어학회논문지
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• 제19권5호
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• pp.890-900
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• 2016

This paper presents a multiple target tracking system using target feature information. In the proposed system, the state of target is defined as its kinematic as well as feature : the kinematic includes a location and a velocity; the feature contains the image correlation between a prior target and a current measurement. The feature information is used for generating the validation matrix and association probability of joint probabilistic data association (JPDA) algorithm. Through the Kalman filter, the target kinematic is updated. Then the tracking information is cycled by the track management algorithm. The system has been evaluated using the images obtained from Electro-Optics/ InfraRed (EO/IR) sensor. It is verified that the proposed system can reduce the complexity burden of JPDA process and can enhance the track maintenance rate.

• 한국항행학회논문지
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• 제1권1호
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• pp.35-46
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• 1997

다중 표적의 추적은 과거 10여년 동안 레이다 용용분야에서 큰 주목을 받아 왔으며 많은 학술회의와 논문발표의 중심 과제가 되어왔다. 이 문제를 해결하기 위하여 여러 가지 추적방식들이 제시되어 왔다. 그 중 대표적인 것으로는 Nearest Neighbor (NN) 방식에 의한 비확률적인 짝배정(association) 방법과 확률적인 표적모형에 기초한 Multiple Hypothesis Test (MHT) 방식 및 Joint Probabilistic Data Association (JPDA) 방식으로 대별할 수 있다. 이러한 여러 가지 방식들은 각기 그 장점 및 단점을 가지게 되어 계산속도나 표적의 추적정확도에 있어서 큰 차이를 나타내게 된다. 본 논문에서는 NN방식, MHT 방식 및 JPDA 필터에 기초한 세 가지 추적 알고리듬을 비교하고, 시뮬레이션을 통하여 다중 기동 표적에 대하여 그 추적성능을 분석한다.

• 한국지능시스템학회논문지
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• 제25권2호
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• pp.186-190
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• 2015

지능형 자동차는 주변 환경에 대한 인식을 바탕으로 동작을 계획하고 움직인다. 따라서 정확한 환경 인식은 자율 주행 자동차의 필수 요소로 여겨진다. 차량의 주행 환경은 차량이나 보행자 같은 동적인 장애물이 다수 존재하여, 안전한 동작을 위해 이런 동적 장애물에 대한 인식이 정확하게 이루어져야 한다. 이를 위해 센서의 불확실성을 극복하는 일이 필수적이다. 본 논문에서는 레이더 센서를 이용하여 다수의 차량을 인식하고 추적하는 알고리즘을 제안한다. 제안된 추적 시스템은 몇 가지 특징을 갖는다. 레이더 센서가 차량을 계측할 때, 그 데이터가 양 모서리에서 주로 나타나는 특징을 혼합 밀도 네트워크로 표현하고, 이렇게 표현된 레이더 데이터의 확률적인 분포를 파티클 필터의 가중치 계산에 적용하여 추적 알고리즘을 수행하였다. 또한, 파티클 필터가 갖는 차원의 저주를 극복하고 시간의 흐름에 따라 그 숫자가 변화하는 다수 대상체의 상태를 예측하기 위해 가역 점프 마르코프 체인 몬테 카를로 (RJMCMC)를 통한 샘플링을 적용하였다. 제안된 알고리즘은 시뮬레이션을 통해 검증되었다.

• 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.

• 한국전자파학회지:전자파기술
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• 제8권1호
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• pp.43-56
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• 1997

다중 표적 추적(MTT:Multiple Target Tracking)은 한 개 또는 그 이상의 센서들을 사용하는 감시(sureillance) 시스템을 위해서 컴퓨터와 마찬가지로 주변상황을 해석하는데 없어서는 안되는 중 요한 요소이다. 레이다, IR(Infrared) 및 Sonar등과 같은 전형적인 센서 시스템들은 여러 가지 신호원 (sources): 문제의 표적, 레이다 지면 클러터(clutter)같은 후면잡음 또는 열잡음같은 내부 오차 요인 으로부터 측정치(measurement)를 만들어 준다. 다중표적용 추적방식의 목적은 센서가 제공하는 측 정 데이터들을 동일한 신호원으로부터 나온 여러 세트의 관측치(observations) 또는 트랙(track)으로 구분해내는 것이다. 이와 같이 일단 트랙이 구성되고 확정되면 후면잡음이나 허위표적을 제거할 수 있 도록 표적의 수를 추산하고 표적의 속도나 예상위치 및 표적의 종류와 기타특성을 계산해낼 수 있다.