• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.6
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• pp.38-46
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• 2008

A distributed system for tracking multiple targets with a pair of multifunction radars is proposed and implemented. The system performs track-to-track association and track-to-track fusion at the fusion center to form fused tracks. The association and fusion are performed using target state information linked via communication nodes from a radar at a remote location. Many factors can affect the track-to-track association and fusion performances. They include delays in data transmission buffer of the remote radar, the error in estimating time-stamp of the remote radar, and the gating in track-to-track association. The effects on association and fusion performances due to these factors are investigated through extensive numerical simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.9
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• pp.863-870
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• 2012

This paper presents a track-to-tack information fusion algorithm using tracks of 2D and 3D radars. Before track fusion, it is needed to match the dimension of the tracks, as the tracks generated by 2D and 3D radars have different dimensions. This paper suggests how the 2D tracks are converted to the 3D tracks for track fusion. Through simulation studies, we can verify that the performance of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.85-92
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• 2008

An architecture for multiple radar tracking systems can be broadly categorized according to the methods in which the tracking functions are performed : central-level tracking and distributed tracking. In the central-level tracking, target tracking is performed using observations from all radar systems. This architecture provides optimal solution to target tracking. In distributed tracking, tracking is performed at each radar system and the composite track information is formed through track fusion integrating multiple radar-level tracks. Track-to-track fusion and track-to-track association are required to perform in this architecture. In this paper, issues and recent research on the two tracking architectures are surveyed.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.126-131
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• 2014

This paper is concerned with a track management method for a naval combat system which receives the tracks information from multi-sensors and multi-tactical datalinks. Since the track management of processing the track information from diverse sources can be formulated as a data fusion problem, this paper will deal with the data fusion architecture, track association and track information determination algorithm for the track management of naval combat systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.335-343
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• 2012

This paper presents an target tracking algorithm for fusion of radar and infrared(IR) sensor measurement data. Generally, fusion methods with Kalman filter assume that processing data obtained by radar and IR sensor are synchronized. It has much limitation to apply the fusion methods to real systems. A key point which is taken into account in the proposed algorithm is the fact that two asynchronous dissimilar data are fused by compensating the time difference of the measurements using radar's ranges and track state vectors. The proposed fusion algorithm in the paper is evaluated via a computer simulation with the existing track fusion and measurement fusion methods.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.34-42
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• 2007

It is very important for the combat system to process extensive data exactly at short time for the better situation awareness compared with the threats in these days. This paper suggests to add radial velocity on the decision factor of sensor data fusion in the existing algorithm for the accuracy enhancement of the sensor data fusion in the combat system.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.17-27
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• 2014

As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.639-645
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• 2010

This paper presents the sensor fusion algorithm that recognizes a primary vehicle by fusing radar and monocular vision data. In general, most of commercial radars may lose tracking of the primary vehicle, i.e., the closest preceding vehicle in the same lane, when it stops or goes with other preceding vehicles in the adjacent lane with similar velocity and range. In order to improve the performance degradation of radar, vehicle detection information from vision sensor and path prediction predicted by ego vehicle sensors will be combined for target classification. Then, the target classification will work with probabilistic association filters to track a primary vehicle. Finally the performance of the proposed sensor fusion algorithm is validated using field test data on highway.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.12
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• pp.1372-1378
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• 2011

The smart air condition system is superior to conventional air condition system in the aspect of control accuracy, environmental preservation and it is foundation for intelligent vehicle such as electric vehicle, fuel cell vehicle. In this paper, failure analyses of smart air condition system will be performed and then sensor fusion technique will be proposed for fail safety of smart air condition system. A sensor fusion logic of air condition system by using CO sensor, $CO_2$ sensor and VOC, $NO_x$ sensor will be developed and simulated by fault injection simulation. The fusion technology of smart air condition system is generated in an experiment and a performance analysis is conducted with fusion algorithms. The proposed algorithm adds the error characteristic of each sensor as a conditional probability value, and ensures greater accuracy by performing the track fusion with the sensors with the most reliable performance.