• Journal of Mechanical Science and Technology
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• v.19 no.9
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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.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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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.

• 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 the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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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.

• Journal of Korea Multimedia Society
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• v.19 no.5
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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.

• Journal of Advanced Navigation Technology
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• v.28 no.5
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• pp.640-649
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• 2024

In this study, we introduce an interactive multi-model-probabilistic data association filter (IMM-PDAF), a multi-target tracking algorithm that integrates multiple dynamic models for accurate real-time maritime target tracking. Multi-target tracking in the maritime environment requires high accuracy due to the complex dynamic environment and various movement patterns. The existing CV-PDAF (constant velocity model) and CT-PDAF (circling model) each assume a constant movement pattern, but it is difficult to handle all the complex movements occurring in various maritime environments with these single models. To solve this problem, this study proposes an interactive multi-model-probabilistic data association filter (IMM-PDAF), and the results of this paper applied to maritime RADAR data show that the proposed IMM-PDAF has relatively lower RMSE values than CV-PDAF and CT-PDAF, and has strong positioning performance even in complex dynamic environments. Therefore, this study results highlight the potential of the proposed IMM-PDAF to improve the reliability and efficiency of maritime surveillance systems and provide a multi-target tracking solution for complex tracking environments.

• Journal of Advanced Navigation Technology
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• v.1 no.1
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• pp.35-46
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• 1997

Over last decade Multiple Target Tracking (MTT) has been the subject of numerous presentations and conferences [1979-1900]. Various approaches have been proposed to solve the problem. Representative works in the problem are Nearest Neighbor (NN) method based on non-probabilistic data association (DA), Multiple Hypothesis Test (MHT) and Joint Probabilistic Data Association (JPDA) as the probabilistic approaches. These techniques have their own advantages and limitations in computational requirements and in the tracking performances. In this paper, the three promising algorithms based on the NN standard filter, MHT and JPDA methods are presented and their performances against simulated multiple maneuvering targets are compared through numerical simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.186-190
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• 2015

Intelligent vehicle plans motion and navigate itself based on the surrounding environment perception. Hence, the precise environment recognition is an essential part of self-driving vehicle. There exist many vulnerable road users (e.g. vehicle, pedestrians) on vehicular driving environment, the vehicle must percept all the dynamic obstacles accurately for safety. In this paper, we propose an multiple vehicle tracking algorithm using microwave radar. Our proposed system includes various special features. First, exceptional radar measurement model for vehicle, concentrated on the corner, is described by mixture density network (MDN), and applied to particle filter weighting. Also, to conquer the curse of dimensionality of particle filter and estimate the time-varying number of multi-target states, reversible jump markov chain monte carlo (RJMCMC) is used to sampling step of the proposed algorithm. The robustness of the proposed algorithm is demonstrated through several computer simulations.

• 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.